ABSTRACT Forced alignment is a speech technique that can automatically align audio files with transcripts. With the help of forced alignment tools, annotating audio files and creating

annotated speech databases have become much more accessible and efficient. Researchers have recently started to evaluate the benefits and accuracy of forced aligners in speech research and

have provided insightful suggestions for improvement. However, current work has so far paid little attention to evaluating forced aligners in prosody research, which focuses on

suprasegmental features. In this paper, we take ambiguous sentence-level audio input in Mandarin Chinese, which can be disambiguated prosodically, to evaluate the alignment accuracy of the

accurate alignment at both syllable and phrase levels for tonal languages, such as Mandarin. We found that the average differences between human annotators and MFA were smaller than the gold

standard, indicating a satisfactory level of performance by the tool. Moreover, the MFA-assisted annotation rate by human transcribers was at least 20 times faster than previously reported

manual annotation efficiency, providing significant time and resource savings for prosody researchers. Our results also suggest that phrase-level alignment accuracy of MFA can be affected by

the quality of the recording, calling prosody researchers’ attention to controlling the audio quality in the recording. The finding that de-stressed words/phrases pose challenges for MFA

also provides a reference for improving forced aligners. SIMILAR CONTENT BEING VIEWED BY OTHERS JOINT SPEECH AND TEXT MACHINE TRANSLATION FOR UP TO 100 LANGUAGES Article Open access 15

January 2025 A DATASET OF REAL AND SYNTHETIC SPEECH IN UKRAINIAN Article Open access 06 May 2025 ASSESSING THE ACCURACY OF AUTOMATIC SPEECH RECOGNITION FOR PSYCHOTHERAPY Article Open access

03 June 2020 INTRODUCTION Speech research often requires detailed annotation on audio files to formulate statistical analysis about the information revealed by the sounds. Annotation tasks

include aligning phoneme, syllable, word, and phrase boundaries so that researchers can extract phonetic and phonological information such as the duration, pitch, and intensity of specific

regions of speech. Manual annotation of audio files is undoubtedly a tedious and resource-consuming task for speech researchers. Researchers need to listen to the audio repeatedly to label

and align the boundaries manually. A 1-min audio file may require hours of annotation work. Moreover, multiple annotators need to work on the same audio annotation and compare the

inter-annotator agreement to avoid human annotation errors. Fortunately, with the rapid development of speech recognition technology since the 1970s, forced alignment, as a technique to

align audio files and create auto-generated annotations, has evolved significantly, which makes automatic annotation possible and efficient. According to Pettarin (2018)’s collections, there

are at least 15 open resource programs. Research has reported highly positive feedback on the performance of forced alignment tools, especially on phoneme boundaries (DiCanio et al., 2013;

Hosom, 2009; Goldman, 2011; Gorman et al., 2011; Yuan and Liberman, 2015; Yuan et al., 2018; Yuan et al., 2014; McAuliffe et al., 2017; Sella, 2018; MacKenzie and Turton, 2020; Mahr et al.,

2021; McAuliffe, 2021; Liu and Sóskuthy, 2022). Combined with the easy accessibility of forced aligners in recent years, forced alignment has drawn increasing attention from speech

researchers and resulted in growing applications of forced aligners in speech research. In this study, we evaluated forced aligners from the perspective of prosodic research for tonal

languages, which has barely been discussed in the literature. Using a forced aligner in Mandarin prosodic analysis, we aim to provide an example of how to use forced alignment in speech

research that investigates suprasegmental-level sound information and how to measure the performance of forced aligners when applied to a tonal language such as Mandarin Chinese. THE TOOL

AND INPUT The purpose of this study was to evaluate the automatic alignment performances for speech prosody research. We chose the Montreal Forced Aligner as the forced aligner tool and

speech data collected from native adult speakers of Mandarin Chinese as the input. MONTREAL FORCED ALIGNER (MFA) MFA is an alignment tool with high accessibility and compatibility (McAuliffe

et al., 2017). It is open-source software with prebuilt executables for both Windows and Mac OSX. MFA has a pre-trained model for some languages, and it can be trained with other languages

that are not included in the model.Footnote 1 As an update of the Prosodylab-Aligner (Gorman et al., 2011), MFA uses more advanced acoustic models based on triphones and is built using the

Kaldi toolkit, offering “advantages over the HTK toolkit underlying most existing aligners” (McAuliffe et al., 2017). Recent research by Mahr et al. (2021) and Liu and Sóskuthy (2022) both

reported highly positive evaluation results on MFA. Mahr et al. (2021) evaluated the performances of popular forced alignment algorithms on children’s American English speech samples and

reported that the aligners’ accuracy ranges from 67 to 86%, compared to the gold-standard human annotation. Among the five forced aligners in their consideration, MFA with speaker-adaptive

training (McAuliffe et al., 2017) was the most accurate aligner across speech sound classes, with an average percentage accuracy of 86%. Liu and Sóskuthy (2022) evaluated the performances of

MFA on four Chinese varieties (Canto, Shanghai, Beijing, and Tianjin) and also found strong agreement between human and machine-aligned phone boundaries, with 17 ms as the median onset

displacement and little variation across the Chinese varieties. Based on reported evaluation results, MFA showed excellent performance in aligning word and phone boundaries in various speech

datasets (McAuliffe et al., 2017; McAuliffe, 2021) and produced higher quality alignments compared to other forced aligners (Gonzalez et al., 2020; Mahr et al., 2021). THE SPEECH DATASET

The dataset for the evaluation of MFA was established in the following way: A total of 33 adult speakers of Mandarin Chinese, aged 18 years or older, were recruited through social media and

contributed speech samples. Fifteen of them (eight females and seven males) recorded their speeches through their phones or laptops and submitted their recordings through emails; the other

18 (nine females and nine males) recorded their speeches in a sound-proof recording booth with professional recording equipment.Footnote 2 Speech samples were recorded based on given target

sentences. Fifty-six target sentences containing _wh_-words (e.g., _sh_é_nme_ ‘what’) were included in the stimuli, with the number of syllables in the sentences ranging from 7 to 20. The

target sentences varied in the position of the _wh_-word (i.e., subject or object) and the structure of the sentence (i.e., simple transitive sentences, simple ditransitive sentences,

sentences with conditional clauses, and sentences with their subject left-dislocated), as shown in Table 1. There were 24 unique sentences in Group 1, 8 unique sentences in Group 2, 8 unique

sentences in Group 3, and 16 unique sentences in Group 4. Each target sentence was potentially ambiguous (Table 1), and participants were provided with two or four possible interpretations

along with the target sentences. For each given meaning of the target sentence, participants were required to read out the target sentence if they thought it could be used to express that

meaning. Otherwise, they could choose to say, “I do not think the target sentence can be used to express the given meaning.” Participants were allowed to update their recording sample of a

sentence anytime by re-recording a sentence before submitting the audio files. After the data collection, to prepare for the audio format required by MFA, we converted all audio files into

WAV files using Praat (Boersma and Weenink, 2021) if they were in a different format. Since Kaldi (the toolkit that MFA was built on) can only process 16-bit files, MFA by default converts

audio files that have higher bit depths than 16-bit into 16-bit. MFA supports both mono and stereo files but by default samples the files with a higher or lower sampling rate than 16 kHz

into 16 kHz during the feature generation procedure; therefore, we did not take steps to normalize the sampling rates, bit depth and channels of all audio files. We then trimmed the audio

files so that only the speech on the target sentences of the experiment was kept. When there were multiple recordings of a target sentence, we only kept the last recording of the sentence in

the dataset. After chopping the long sound files into separate audio files by sentence, for each speaker, 128 recorded audio files were included in the dataset, with 48 audio files from

Group 1, 16 audio files from Group 2, 32 audio files from each of Group 3 and Group 4. One speaker’s submission (Speaker 5) was not included in the dataset due to incomplete recordings. In

total, we collected 4096 audio files from 33 speakers with the duration of audio files ranging from 1 s to 4 s, in total resulting in a 216-min-long speech dataset. We further coded the

audio files to identify the audio files that do not match the given target sentences, such as missing words, repeating words, paraphrasing words, and adding extra words. Each audio file was

coded with a label corresponding to the target sentence and specified reading, otherwise “null” if the speaker said “I do not think the target sentence can be used to express the given

meaning” or “misread” if the sentence that the speaker recorded did not match the target sentence. When generating MFA alignment and evaluating the MFA alignment results, audio files with

“null” or “misread” labels were excluded. Although it is not spontaneous speech data collected from a natural conversation environment, this dataset has three main advantages. First, it

contains minimal pairs of naturally occurring ambiguous sentences that are prosodically distinguished. Take (1), a sentence from Group 3 as an example. (1) _Zhōng.guó-duì_ _shuí_ _yě_

_dǎ-bù-guò_   Chinese-team who also beat-not-complement   a. _wh_-indefinite:    (i) ‘The Chinese team can’t beat anyone.’ or    (ii) ‘No one can beat the Chinese team.’   b.

_wh_-interrogative:    (iii) ‘Who is the team that the Chinese team also can’t beat?’ or    (iv)‘Who is the team that also can’t beat the Chinese team?’ Sentences like (1) are often used by

native speakers in social media to express (1a-i) when talking about the Chinese male soccer team losing all the games or to express (1a-ii) when talking about the Chinese ping-pong team

winning all the games. Wu and Yun (2022) found that sentences like (1) can also induce interrogative readings as in (1a-iii) and (1a-iv). It is important to note that the various readings of

(1) share the same underlying phonemic tones and syllables. However, these tones and syllables are realized with variations in order to achieve different readings. For example, the

_wh-_word “shuí” is typically associated with a rising tone. However, it is commonly realized with a neutral tone or a tone that only slightly rises in pitch when it refers to

_wh_-indefinite, as in (1a-i) and (1a-ii). Similarly, the adverb “yě” typically has a tone that falls before rising in pitch. Yet, in the _wh_-indefinite contexts, it is often produced with

a shorter duration and a tone that only partially falls before rising. The four readings of (1) can also be distinguished by the length of the pause between the noun phrase and the

_wh_-word, and between the _wh_-word and the adverb. Figure 1 displays an example of how these four meanings of (1) may be phonetically and prosodically different. Since the ambiguous

sentences share the same underlying phonemic tones for different meanings (i.e., same transcript and same dictionary input to MFA) but can nevertheless be realized with different phonetics

depending on the meaning, the data provide an excellent opportunity to evaluate how well MFA can handle some departure in production from the expected dictionary pronunciation of these

words. Second, the dataset contains sentence-level speech production with words that have a neutral tone and _er_-suffixation and thus can help evaluate whether the forced aligner can

correctly align sentences with various word types. The stimuli included words with neutral tones, such as the perfective marker _le_, in addition to words with the canonical four tones in

Mandarin. In most cases, one character corresponds to one syllable, but _er_-suffixation makes two characters correspond to one syllable. Words with neutral tones and _er_-suffixation are

often less salient in prosody and may make syllable boundaries less clear, which poses challenges for forced alignment. Third, it is a balanced dataset that has audio files from both lab

recordings and non-lab recordings, and a similar amount of audio files from both female and male speakers. Thus, it provides a potential way to evaluate if the audio input quality and gender

affect the alignment results. EVALUATIONS Using the audio data introduced in section “The tool and input” as input, we conducted forced alignment using MFA at two different levels:

syllable-by-syllable alignment and phrase-by-phrase alignment. Then we assessed each alignment result to evaluate the performance of MFA in terms of annotation accuracy and annotation speed.

SYLLABLE-BY-SYLLABLE ALIGNMENT ACCURACY MFA, like many other alignment tools, takes audio files (.wav), their accompanying transcript files, and a pronunciation dictionary (.txt) as input.

Since there was no ready-to-use MFA pronunciation dictionary for Mandarin in the MFA version 1.0 repository, we followed the format of the prepared Mandarin dictionary example in the MFA

tutorial guide and created a pinyin pronunciation dictionary for the alignment task (Table 2). Considering that one Chinese character corresponds to one syllable, we used _Xiàndài Hànyǔ

Chángyòngzì Biǎo_ ‘_3500 commonly used Chinese characters_’, published by the Ministry of Education in China in 1988, to create the dictionary.Footnote 3 Table 2 presents examples of the

entries in the dictionary. The first column in the dictionary was the syllable’s pinyin, and the second column contained the phonemes with tones attached to the nuclear vowels. The tones

were presented in numbers: 1, 2, 3, and 4, meaning a high-level tone (a.k.a. the first tone), a rising tone (the second tone), a fall-rise tone (the third tone), and a falling tone (the

fourth tone), respectively. For the readers’ information, we also provided one of the possible meanings associated with the pinyin pronunciation in Table 2. The transcripts were also

presented in pinyin, with spaces between each syllable. Table 3 presents a sample of the transcript of a Group 3 type sentence that we used in this experiment. For the readers’ information,

we also provided one of the possible meanings associated with the transcript in Table 3. The output of MFA alignment is annotation files (.textgrid), where phonemes and syllables are

time-aligned. We used the pinyin system in both input and output: Chinese characters were romanized and accompanied by a number to indicate their tones. Figure 2 displays an audio file with

its MFA annotation. When generating MFA alignment output, audio files with “null” (i.e., when the speaker stated that “I do not think the target sentence can be used to express the given

meaning”) and “misread” (i.e., when the speaker recorded a sentence that was different from the given stimuli) were not included. The human alignment output was created upon the MFA

alignment by four well-trained human annotators (also authors of the article) reviewing the boundaries produced by MFA (i.e., boundaries on the “words” tier in Fig. 2) and adjusting

boundaries and interval labels if the MFA-generated boundaries did not match the syllables as intended. We randomly selected 1120 audio files and evaluated the syllable alignment results

that MFA generated, roughly 27.34% of all collected data. Before calculating the accuracy, we first compared the number and the label of intervals in the human alignment and the MFA

alignment on each audio file. If there was any mismatched number or label in the human alignment and the MFA alignment on the same audio file, the entire textgrid files were excluded from

the final comparison. 130 audio files were excluded according to this criterion; in most cases, MFA results have more intervals due to spurious pauses (silent intervals). In the end, the

total number of syllable-level human-aligner pairs that were included in the comparisons is 10,487. To compare the MFA alignment output against human alignment output, we measured the

absolute syllable boundary-time difference for every interval. The comparison results are shown in Table 4. Each cell of the table indicates the group of sentences produced by a specific

speaker (F: female, M: male). _Lab speaker N_ means that the speaker is the _N_th participant who attended the lab recordings. All other recordings were conducted with the speakers’ laptops

or phones. In the average row, the values are presented in milliseconds (ms); the value outside of the parentheses is the average absolute boundary difference and the value within the

paratheses is the standard deviation (SD) for that specific group of data. As we can see from Table 4, MFA produced quite decent syllable-aligned results for Mandarin Chinese, with the

average human-MFA alignment difference being 15.59 ms (SD = 30.41) The average human-MFA syllable-level alignment difference of a group of sentences by a specific speaker ranged from 2.94 ms

to 28.58 ms. Although there were many boundaries that annotators did not need to adjust the MFA-generated ones, there were also many boundaries where annotators needed to make significant

adjustments, yielding high standard deviations in the results. To dig into the causes of the high standard deviations, we further examined the 197 data points where the absolute time

difference is larger than 100 ms. We found that most of these outliers involved the boundaries for _wh_-word (_shuí_, ‘who’), adverb (_yě_, ‘also’) and negation marker (_meí_, ‘not’), and

words with third tones and neutral tones (e.g., _zhě_, _wěi_, _nǎ_, _gěi_, _me_, _le_, _de_, _yǐng_) in the Group 1 and Group 4. We will delve into a detailed discussion to explain why these

certain words can pose challenges for forced alignment in section “Discussion”. Moreover, following the conventional practice of forced aligner evaluation, we chose 25 ms as a threshold to

transform the numerical data into binary data. This binary labeling, “YES” for MFA-human absolute time differences below 25 ms and “NO” otherwise, allowed us to calculate agreement rates. We

then conducted chi-square tests on the binary data to assess statistical significance. The 25 ms proposed by McAuliffe et al. (2017) is the gold-standard threshold used to evaluate the

phone-onset human-aligner difference and has been employed in research on forced aligner evaluation, including Mahr et al. (2021). The evaluation of the data revealed that 73.49% of

human-MFA differences at the syllable level were within 25 ms of the gold standard, indicating a high level of agreement. This rate closely approximates the phone-level agreement rate (77%

within 25 ms) reported by McAuliffe et al. (2017) and is significantly higher than the phone-level agreement rate (64% within 25 ms) reported by Mahr et al. (2021).Footnote 4 The results of

the current test show that MFA can make decent syllable-level alignment for a tonal language like Mandarin, too. Furthermore, MFA is established to correctly align audio files of ambiguous

sentences with underlying phonemic tones but different prosodic markings. Additionally, the average syllable boundary-time difference between humans and MFA for audio files recorded through

phones or laptops (“local recordings”) was 17.02 ms (SD = 31.41). It is numerically higher than the average human-MFA time difference for audio files recorded in a professional recording

booth (“lab recordings”), which was 13.80 ms (SD = 29.02). 71.31% of the human-MFA difference for local recordings was smaller than 25 ms, while 76.20 % of the human-MFA difference for lab

recordings was smaller than 25 ms. There is a significant difference between the local recordings and lab recordings on the 25 ms threshold via a chi-square test (_p_ < .001). This

alignment evaluation shows that the audio input from phones or laptops yields decent MFA syllable-alignment accuracy but is still not as good as the lab recordings, which is consistent with

the findings of Sanker et al. (2021): local recordings are feasible and reliable to get segment boundaries but lab recordings are always better. For all the recordings evaluated in this

test, the overall average human-MFA difference for audio files from female speakers was 15.74 ms (SD: 32.29) with 73.66% of the difference smaller than 25 ms, and the overall average for

male speakers was 15.48 ms (SD: 29.08) with 73.38% of the difference smaller than 25 ms, with no significant effect of gender observed (_p_ > 0.05). EFFICIENCY Previous research mainly

focused on evaluating the alignment accuracy of forced aligners and rarely tested their efficiency. To estimate the MFA-aided syllable-level annotation efficiency, we timed one session of

the syllable-level accuracy evaluation tasks to see how much syllable-level annotation work could be done with MFA in 30 min. The timed assessment was conducted by two of the four expert

human annotators who have been working on the accuracy evaluation. Human annotator X used a Lenovo Legion 5 15IMH05 laptop with Windows operating system, Intel Core i5-10300H CPU @ 2.50 GHz,

and 8GB RAM, while human annotator Y used a MacBook Pro with macOS operating system, 1.4 GHz quad-core Intel Core i5, and 8GB RAM. Both laptops had robust computational and processing

capabilities and were connected to high-speed internet. Before the test, MFA was pre-installed on the annotators' laptops, and the audio files, pronunciation dictionary, and transcripts

were finalized and available in a shared online folder. The annotators were instructed to randomly select a set of audio files from the same speaker, which they had not evaluated, and copy

them to their local working folder to start the timer. Human annotator X chose 32 audio files from Speaker 9 Group 4, with three files labeled as “null” or “misread.” After spending 6 min

copying the required files to a local folder and activating MFA, this annotator generated TextGrid files for the 29 non-null/non-misread audio files, each about three seconds long and

totaling 100 s, and spent 3 min logging annotation notes. In the remaining 21 min, this annotator completed annotations for 14 out of the 30 audio files, finalizing the TextGrid files after

manually adjusting the results generated by MFA. Human annotator Y selected 48 audio files from Speaker 9 Group 1, with no files labeled as “null” or “misread.” After spending 4 min copying

the required files to a local folder and activating MFA, this annotator generated TextGrid files for all 48 audio files, each between two and three seconds long and totaling 131 s, and spent

3 min logging annotation notes. In the remaining 23 min, this annotator finalized annotations for 14 out of the 48 audio files. It is important to note that the results of this efficiency

test depict a relatively optimal scenario, given that the two expert human annotators had prior experience with Praat and MFA as well as the MFA-aided annotation workflow, and the laptops

utilized had sufficient computational and processing capabilities. Nevertheless, as we have seen, a significant amount of time and resources are still saved for speech researchers with the

help of MFA. According to the current efficiency test, MFA-aided annotation took about 30 to 40 min to complete annotation with both phone-level and syllable-level information for a

1-min-long recording. In contrast, literature reported that manual annotation at the phone level can take up to 13 h for a 1-min recording, which is 800 times the duration of the audio

(Goldman, 2011; Schiel et al., 2012: 111). PHRASE-BY-PHRASE ALIGNMENT Prosodic research often focuses on phrase-level intonation and pitch contours. Therefore, the annotation of audio files

should include phrase boundaries. With the help of MFA, the tedious manual annotation work for prosodic research can be significantly simplified. Human annotators do not need to start by

creating an empty annotation file (.textgrid) and draw boundary lines for phrases one by one. Instead, human annotators can take the syllable-level annotation like the one in Fig. 2 as a

starting point and make two steps of small adjustments: first, change selected syllable boundaries to phrase-level boundaries, and second, change the labels at the words-tier to linguistics

phrase labels that fit the prosody research purpose, such as “subject,” “pause”, and “_wh_.” The final product after this adjustment process is shown in Fig. 3. While the existence of

syllable-level segmentation already simplifies the phrase-level annotation process, can it be further simplified? Can we make MFA generate phrase-level aligned boundaries directly? These

questions motivated us to conduct another test with MFA to see whether it can generate phrase-level boundaries with decent alignment accuracy and efficiency. The definition of phrases in

prosody research depends on the research question and the target of analysis. For example, we can divide the sentence in (1) into three phrases: “pre-_wh_ region,” “_wh_-word,” and

“post-_wh_ region” if the primary focus of the prosodic research is to compare the acoustic properties of the pre-_wh_ region and the post_-wh_ region. Alternatively, (1) can be divided into

four phrases: “subject,” “_wh_,” “adverb,” and “verb-negation-complement” if the prosodic research intends to investigate the adverb and the verb phrase separately in the post-_wh_ region.

In this phrase-level alignment test, we used the latter phrasing format. We expect that MFA can exhibit similar phrase-level performance when the first phrasing format is used. (1) [PRE-_WH_

REGION] [_WH_] [POST-_WH_ REGION]     [SUBJECT] [_WH_] [ADVERB] [VERB-NEGATION-COMPLEMENT]   _Zhōng.guó-duì_ _shuí_ _yě_ _dǎ-bù-guò_   Chinese-team who also beat-not-complement   a.

_wh_-indefinite:    (i) ‘The Chinese team can’t beat anyone.’ or    (ii) ‘No one can beat the Chinese team.’   b. _wh_-interrogative:    (iii) ‘Who is the team that the Chinese team also

can’t beat?’ or    (iv) ‘Who is the team that also can’t beat the Chinese team?’ THE FORMAT OF TRANSCRIPTS AND DICTIONARIES The Mandarin Chinese transcripts and pronunciation dictionary used

in section “Syllable-by-syllable” alignment follow a character-by-character (i.e., syllable-by-syllable fashion) fashion instead of a word-by-word fashion. It is due to the fundamental

differences between English and Mandarin: there are no noticeable word boundaries in the Chinese writing system, while one Chinese character normally corresponds to one syllable. To get

phrase-by-phrase-aligned boundaries or at least word-by-word boundaries in Mandarin, we started to experiment with MFA by varying the transcript and dictionary input, testing different

combinations on the audio files that were randomly selected from the dataset. For each combination, we tested audio files from Speaker 1, Speaker 8, and Speaker 12 across different groups of

stimuli. We first tried the combination of a phrase-level transcript (Table 5) and the syllable-level dictionary used in section “Syllable-by-syllable alignment” (Table 2). Phrase-level

transcripts were created manually according to the annotation purposes of our prosody project. For example, since the prosody project aims to analyze the prosodic features of _wh_-phrases,

an ideal annotation product would be to consider a complex _wh_-phrase like _nǎ-gè-duì_ ‘which/any team’ as one unit. As in Table 5, the phrase-level transcripts have an empty space in

between two phrases. However, this kind of transcript and dictionary combination did not produce proper alignments. Figure 4 presents the test results of this combination on an audio file

from Speaker 12, Group 2 recording, which refers to the sentence meaning ‘Wangxin did not send anything to Xufang the day before yesterday’.Footnote 5 Note that the syllable-level dictionary

used in section “Syllable-by-syllable” alignment was generated through the 3500 Commonly Used Chinese Character list. As the MFA tutorial guide also mentioned a way to generate a dictionary

from a transcript, we were wondering if a dictionary generated from a small training set produces different phrase alignment results. So, we generated a syllable-level dictionary based on

the syllable-level transcripts and referred to this dictionary as the “small-set” syllable-level dictionary, to distinguish it from the “big-set” syllable-level dictionary (i.e., the one we

used in section “Syllable-by-syllable alignment”).Footnote 6 The two kinds of syllable-level dictionaries have the same format as in Table 2 but only differ in the size of the dictionary.

However, the combination of the “small-set” syllable-level dictionary and the phrase-level transcript (Table 5) did not produce satisfactory results either. Figure 5 presents the test

results of this combination on the same audio file we used to generate Fig. 4. The unsatisfactory alignment results which are shown in both Figs. 4 and 5 suggest that syllable-level

pronunciation dictionaries are not suitable for phrase-level alignment. Therefore, we switched to phrase-level dictionaries in the following trials. In the next trials, we tested the

combinations of phrase-level dictionaries (Table 6) and phrase-level transcripts (Table 5). When creating the phrase-level dictionaries, we explored two ways: one with each phoneme separated

in the description of pronunciation and another with each syllable separated, as in the first row and second row of Table 6, respectively. Figures 6 and 7 present the test results of these

two dictionaries combined with a phrase-level transcript on the same audio file that was used to generate Figs. 4 and 5. The combination of the phrase-level transcript and the phrase-level

dictionary with phoneme-by-phoneme pronunciation seems to give the best alignment results among all the trials, as in Fig. 6. Therefore, we decided to further explore the potential of using

this combination as the input to MFA for generating phrase-level boundaries for prosodic research. ACCURACY As in the previous assessment described in section “Accuracy”, we took audio files

(.wav files) and their accompanying transcripts as well as pronunciation dictionaries (.txt files) as the input; and excluded audio files with “null” or “misread” labels. The only

difference is that the transcripts and pronunciation dictionaries are both phrase level, in the same fashion as what we saw in the first row of Table 6. Figure 6 is an example of MFA

phrase-level annotation results shown in Praat. We used the same 1120 audio files used in the syllable-level evaluation and evaluated the phrase alignment results that MFA generated, roughly

27.34% of all collected data. Human annotation results were created based on MFA annotation by well-trained human annotators reviewing the boundaries produced by MFA (i.e., boundaries on

the “words” tier in Fig. 6) and adjusting boundaries and interval labels if the MFA-generated boundaries did not match the phrases as intended. We applied the same data trimming process and

criteria to the phrase-level annotation results as we did to the syllable-level annotation results. There were 354 audio files involved with at least one mismatched number or label of

intervals in the human alignment and the MFA alignment; in most cases, MFA results and human results have different numbers of silent intervals. After excluding the files with mismatched

sizes and mismatched labels, the total number of phrase-level human-MFA pairs that were included in the comparisons is 3944. The focus of comparison was the absolute phrase boundary-time

difference of each phrase between the MFA alignment and human annotation. The comparison results are shown in Table 7. The same notational conventions for Table 4 are applied to Table 7. As

we can see from Table 7, MFA produced quite decent phrase-aligned results for Mandarin Chinese, with the average human-MFA alignment difference being 22.49 ms (SD = 38.39) The average

human-MFA phrase-level alignment difference of a group of sentences by a specific speaker ranged from 1.33 ms to 282.11 ms. In line with our observations at the syllable level, the

annotation process also revealed instances where the phrase boundaries generated by the MFA did not require any adjustment by the annotators. However, there was also a portion of instances

where significant adjustments were necessary, resulting in high standard deviations and variations in the average time differences among the results. To understand the reasons for the high

standard deviations and variations, we analyzed 106 data points where the absolute phrase boundary-time difference exceeded 100 ms. Out of these, six data points in Speaker9_Group4_M had

extremely high human-MFA time differences of up to 300–700 ms, which were manually checked and found to be an apparent mistake in MFA. The remaining outliers were primarily related to phrase

boundaries with neutral tones and tone sandhi in Group 1 and Group 4 stimuli. These types of words can be challenging for forced alignment because they often have subtle variations in

pronunciation and timing depending on the surrounding context. Additionally, these types of words may have different prosodic properties, such as pitch and intonation, which can further

complicate the alignment process. We will provide an in-depth discussion of this matter in section “Discussion”'. To determine the level of agreement between the MFA and human

annotators at the phrase level, we followed the same data processing procedure as we did for syllable-level evaluation and transformed the time differences into binary data using a 25 ms

threshold. We found that 65.57% of the human-MFA differences at the phrase level were within 25 ms of the gold standard. It is important to note that the number of human-MFA comparison pairs

is influenced by the complexity of phrase boundaries, which may involve suprasegmental features such as intonation and pitch variations. As such, we cannot directly compare the overall

average phrase alignment accuracy to the overall average syllable alignment accuracy reported in this study and previous studies. However, we can reasonably expect a lower human-MFA

agreement at the phrase level compared to the syllable or phone level. Nevertheless, the observed 65.57% agreement rate at the phrase level is considered a very decent level of agreement,

especially when compared to the phone-level agreement rate (64% within 25 ms) reported by Mahr et al. (2021). Moreover, using MFA still provides great help because creating all the phrase

boundaries manually would take a great amount of time and effort. Instead of starting from scratch, letting MFA create the phrase boundaries automatically and later find and fix any errors

would enhance the annotation efficiency to a great extent, as illustrated in section “Efficiency”. Additionally, we found some influence of the recording quality on the accuracy rate of the

phrase-by-phrase alignment. The overall average phrase boundary-time difference for local recordings was 25.86 ms (SD = 42.48) with 57.48% of the difference smaller than 25 ms. The overall

average phrase boundary-time difference for lab recordings was 16.86 ms (SD = 38.87) with 74.16% of the difference for lab recordings. The local recordings demonstrated significantly lower

phrase alignment accuracy compared to the lab recordings on the 25 ms threshold via a chi-square test (_p_ < 0.001). The results of the phrase-by-phrase alignment analysis present a

sharper contrast between lab recordings and local recordings than what we observed in the syllable-level alignment analysis. This finding further emphasizes that a more professional

recording environment is preferable when researching prosodic phrase boundaries. Among all audio files evaluated in this test, the overall average human-MFA difference for audio files from

female speakers was 17.64 ms (SD: 32.31) with 71.95% of the difference smaller than 25 ms, and the overall average for male speakers was 24.13 ms (SD: 41.85) with 61.20% of the difference

smaller than 25 ms. A significant effect of gender on the MFA accuracy was observed (_p_ < 0.001). The interaction between the gender of the speaker and the alignment accuracy indicates

that phrase-level boundary alignment is noticeably difficult in male speech. Since prosodic boundaries are sensitive to suprasegmental features such as pitch and intensity (Wagner and

Watson, 2010), we conjecture that the detection of prosodic boundaries is challenging when the voice pitch is low. The phrase-by-phrase alignment results suggest that researchers should

choose a more professional recording environment if the research concerns prosodic phrase boundaries. EFFICIENCY To evaluate MFA-aided phrase-level annotation efficiency, we timed one

session of the phrase-level accuracy evaluation tasks to see how much phrase-level annotation work could be done with MFA annotation in 30 min using phrase-level transcripts and

dictionaries. Two well-trained human annotators attended this timed evaluation. They were the same human annotators using the same laptops who participated in the time evaluation for

syllable alignment in section “Efficiency”. Similar to the time evaluation in section “Efficiency”, MFA and required files were prepared before the efficiency test. They were asked to choose

a set of audio files from the same speaker and to start the timer once they began to copy the selected audio files to the working folder. Human annotator X selected the recordings from Lab

Speaker 6, Group 3, where 32 audio files were in the folder with 13 audio files labeled “null” or “misread.” This human annotator spent 8 min copying required files to a local folder,

activating MFA, and using MFA to generate all the TextGrid files for the 19 non-null or non-misread audio files (in total 54 s long with each file being about 3 s long) and 3 min logging

annotation notes. During the remaining 19 min, this human annotator was able to complete annotations for 14 out of the 19 audio files, which means that the TextGrid files were finalized for

these 14 files after human annotators’ manual adjustments to the annotation results generated by MFA. Human annotator Y selected the recordings from Lab Speaker 6, Group 2, where 16 audio

files were in the folder with 0 audio files labeled “null” or “misread.” This human annotator spent 5 min copying required files to a local folder, activating MFA, and using MFA to generate

all the TextGrid files for the 16 non-null or non-misread audio files (in total 50 s long with each file being about 3 s long) and 3 min logging annotation notes. During the remaining 22 

min, this human annotator was able to complete annotations for 12 out of the 16 audio files. Although we cannot make a direct comparison regarding efficiency between syllable-alignment and

phrase-alignment, the two human annotators both reported that they thought a phrase-level dictionary and transcript was better than a syllable-level dictionary and transcript for improving

annotation efficiency for a prosody project that requires phrase-by-phrase annotation. Human annotators’ feedback makes us optimistic about the application of MFA with a phrase-level

transcript and dictionary in Mandarin prosody research. DISCUSSION We evaluated the MFA-generated syllable-level results and phrase-level results manually to compare the human-MFA annotation

difference, as reported in section “Syllable-by-syllable alignment”. While the results suggested that using MFA would make the annotation task much more effective and efficient, we found

that it showed less satisfactory performance when complex or non-salient acoustic features were involved. The most common error for MFA’s syllable-level alignment was found for syllables

with a third tone, such as _zhě_ (‘person’)_, wěi_ (‘person given name’)_, nǎ_ (‘which’)_, yě_ (‘also’)_, gěi_ (‘for’)_, diǎn_ (‘a little’), or those with a neutral tone, such as _le_ (an

aspect marker in Mandarin) and _de_ (a complement marker). This finding is compatible with the unique acoustic features of third tones and neutral tones. Among the four basic tones in

Mandarin, the third tone is different from the other three tones because it is internally a combination of falling-rising tones. Moreover, the third tone is often realized with a rising tone

before another syllable with a third tone due to the tone change process called “third tone sandhi.” The acoustic characteristics of the third tone and tone sandhi are considerably complex,

making the alignment performance less accurate than others. The characteristic of a neutral tone is that it is “de-focused”, meaning that you do not have to put extra stress on the syllable

but only need to give it the same amount of stress as what has been given to the preceding syllable. Neutral tones are also often shorter than the other tones. These acoustic features of

neutral tones (i.e., short and non-salient) can be the primary reason why MFA often shows alignment errors on the boundaries of syllables with neutral tones. The most common error for MFA’s

phrase-level alignment was found when phrases contained functional words. Functional words are words that have a grammatical purpose, such as classifiers (e.g., _wèi, gè_), negation markers

(e.g., _méi, bù_), adverbs (e.g., _yě_), determiners (e.g., _diǎn_), prepositions (e.g., _gěi_), aspect markers (e.g., _guò_), and adverbial clause markers (e.g., _rúguǒ_, _dehuà_). In

Mandarin, frequently used functional words with regular tones often have reduced pronunciations in speech production. Monosyllabic functional words, such as _yě_ ‘also’ typically have only

one syllable and that syllable is often unstressed and phonetically reduced. Similarly, the second syllable in disyllabic functional words, such as _rúguǒ_ ‘if’, is often pronounced with

reduced stress and phonetic prominence (Lai et al., 2010; Yang, 2010, Třísková, 2016). Syllables with neutral tones and phrases with functional words share the same acoustic feature: being

de-stressed. Therefore, MFA does not perform well in these de-stressed parts for both syllable-level and phrase-level alignment. The finding calls for improvement of forced aligners,

including MFA concerning alignments for de-stressed speech. Pronunciation variations and _er_-suffixation (also known as “Erhua” in the literature) are two other challenges for forced

alignment at both the syllable and phrase levels. Examples of pronunciation variations include the _wh_-words _shuí_ (‘who’) and _nǎ_ (‘which’) are sometimes pronounced as _sheí_ and _něi_

in speech production due to dialectal influence. We observed that MFA shows lower accuracy for audio files where speakers pronounced the words with such variants. _Er_-suffixation refers to

a phonological resyllabification in Mandarin. In the data we evaluated, the syllable _er_ and the syllable _diǎn_ (from Group 2 Stimuli) are often combined into one syllable in speech

production by speakers. This phonological resyllabification process not only changes the syllable boundary but also influences the tone and phonetic realization of the _er_-suffixed forms

(Huang, 2010). What makes the _er_-suffixation process more complicated is that it may be implemented differently in dialects of Mandarin (Jiang et al., 2019). This finding further

underscores the necessity of forced aligners, including MFA, to take into account dialectal influences and speech variations among speakers. By recognizing and accounting for these

variations, forced aligners can more accurately align speech signals with their corresponding text and improve the overall performance of automatic speech recognition systems. CONCLUSION

This paper presents the first detailed evaluation reports of MFA regarding its alignment accuracy and efficiency at both the syllable level and phrase level for prosody research in Mandarin

Chinese, a tonal language. In both syllable-alignment and phrase-alignment tasks, the average differences between human annotators and MFA were smaller than the gold standard (25 ms).

Furthermore, MFA-aided annotation by human transcribers was at least 20 times faster than the manual annotation that has been previously reported. Although MFA showed less accuracy for the

non-salient acoustic features, speech with non-standard pronunciations, and audio files with lower quality, its decent alignment accuracy and high efficiency indicate that it would be useful

for simplifying the annotation process in prosody research, even when dealing with tonal languages like Mandarin. The recording quality significantly affects both MFA’s syllable-level and

phrase-level alignment, emphasizing the need to control audio quality in prosody research when using MFA. Additionally, a gender effect was observed in MFA’s phrase-level alignment,

highlighting the importance of addressing gender effects in prosody research. Our study proposes a new workflow for conducting prosodic research in tonal languages. This approach involves

using MFA for phrase-level annotation, followed by necessary adjustments made by human annotators. By adopting this workflow, researchers can save considerable time and resources that would

otherwise be required for manual human annotation. This will have a significant impact on the field of prosodic research, as it will enable researchers to investigate the role of prosody in

tonal languages more efficiently and effectively. The finding that de-stressed words and phrases, as well as pronunciation variations, pose challenges for MFA provides a reference for

improving forced aligners. With continuous advancements in forced aligner technology, we expect a lower human-MFA syllable-/phrase-boundary difference and a higher percentage of the

differences being smaller than the gold standard. As we were finalizing our paper, MFA released version 2.0 of its acoustic model, along with a Mandarin (Erhua) pronunciation dictionary. The

new dictionary includes additional pronunciations, such as reduced variants commonly found in spontaneous speech, where segments or syllables are deleted. This update is expected to improve

the performance of MFA, particularly in areas such as _er_-suffixation. We look forward to testing this conjecture in follow-up studies and expanding the current research into other tonal

languages, thereby offering a systematic and comprehensive reference for the advancements in forced aligner technology. DATA AVAILABILITY A complete list of target sentences, and the data

for syllable-level, and phrase-level comparisons are available at the Dataverse repository: https://doi.org/10.7910/DVN/EV4KAN. The audio files and annotated text-grid files generated during

the current study are not publicly available due to them containing information that could compromise research participant privacy and consent but are available from the corresponding

author on reasonable request. NOTES * The MFA tool we used in the paper was MFA version 1.0. It contains a pre-trained grapheme-to-phoneme conversion (G2P) model for Mandarin Chinese that

supports both characters and pinyin, as well as a pre-trained acoustic model for Mandarin. It contains an example of a Mandarin pronunciation dictionary but not a full-scale, ready-to-use

one; thus, we created our own pronunciation dictionary, as explained in section “Evaluations”, following the MFA’s user guide. At the time we were writing up the paper, MFA released updates

for version 2.0 in June 2022. According to the latest release of MFA G2P models, there is no Mandarin G2P 2.0 model available at this moment, while the Mandarin MFA dictionary, version 2.0

version, and the Mandarin MFA acoustic model, version 2.0. have been released. * For the non-lab recordings, we instructed the speakers to record the sentences in a quiet room, without

specifying any recording parameters for their phones or laptops. The iPhone models used by Speakers 1, 3, 4, 6, 7, 8, 11, 12, and 15 included the iPhone 6 s, iPhone 11, iPhone 12, iPhone X,

iPhone SE 2, and iPhone 12 pro. Speaker 2 and 14, respectively, used Huawei Mate 20 and Samsung S20 phones, while Speaker 9 and 13 used Lenovo laptops for recordings. All of the devices used

by non-lab speakers had built-in microphones that produced high-quality audio files. The lab recordings were made in a sound-proof room using an Audio-Technica AT4050 microphone and a Denon

Recorder (DN-700r). By default, the recorder produces audio files in MP3 format at 128kbps with 44.1 kHz stereo sampling rate. When evaluating the MFA results, we did not distinguish

between mobile phone and laptop recordings as the non-lab recordings we collected were mostly mobile phone recordings. While using audio files with varying quality can help assess how well

MFA can handle such files, we acknowledge that different audio signal characteristics can yield different results. In future studies, we plan to employ a fully controlled experiment design

with balanced data to thoroughly examine how audio signal characteristics affect forced aligner accuracy and efficiency. * This file with brief English instructions can be accessed at

https://lingua.mtsu.edu/chinese-computing/statistics/char/listchangyong.php. * The existing MFA evaluation research focuses on the phone-onset difference, with no direct syllable-boundary

evaluation results that we can use as a reference. However, syllable-boundary can be more complicated than phone-onset, as it may involve syllable-level acoustics features like stress. With

that being said, the results of the current test are very promising. * The label “spn” in MFA is used for modeling unknown words. However, we observed that when MFA encounters a sound that

it cannot recognize accurately, it assigns the “spn” label to the corresponding interval. For example, although the syllable _de_ with a neutral tone in Mandarin is present in the transcript

and dictionary input, MFA often labels the corresponding phonemes as “spn.” * The usage of MFA to generate the dictionary does not involve training the acoustic model. Instead, we leverage

the pre-trained g2p model in MFA for this purpose. (https://montreal-forced-aligner.readthedocs.io/en/latest/first_steps/example.html#dict-generating-example) REFERENCES * Boersma P, Weenink

D (2021) Praat: doing phonetics by computer [Computer program], Version 6.1.53, retrieved 23 Sept 2021 from. https://www.praat.org * DiCanio C, Nam H, Whalen DH, Bunnell HT, Amith JD,

García RC (2013) Using automatic alignment to analyze endangered language data: testing the viability of untrained alignment. J Acoust Soc Am 134(3):2235–2246.

https://doi.org/10.1121/1.4816491 Article  ADS  PubMed  PubMed Central  Google Scholar  * Goldman JP (2011) EasyAlign: an automatic phonetic alignment tool under Praat. Proceedings of the

12th Annual Conference of the International Speech Communication Association, Florence, Italy, pp. 3233–3236. August 28–31, 2011 Google Scholar  * Gonzalez S, Grama J, Travis C (2020).

Comparing the performance of forced aligners used in sociophonetic research. Linguist Vanguard. 5. https://doi.org/10.1515/lingvan-2019-0058 * Gorman K, Howell J, Wagner M (2011)

Prosodylab-aligner: a tool for forced alignment of laboratory speech. Can Acoust 39:192–193 Google Scholar  * Huang T (2010) _Er_-suffixation in Chinese monophthongs: phonological analysis

and phonetic data. In: Clemens LaurenE, Liu Chi-MingLouis eds Proceedings of the 22rd North American Conference on Chinese Linguistics (NACCL-22) and the 18th International Conference on

Chinese Linguistics (IACL-18). Harvard University, Cambridge, MA, pp. 331–344 Google Scholar  * Hosom JP (2009) Speaker-independent phoneme alignment using transition-dependent states.

Speech Commun 51:352–368 Article  PubMed  PubMed Central  Google Scholar  * Jiang S, Chang Y, Hsieh F (2019) An EMA study of Er-suffixation in Northeastern Mandarin monophthongs. In: Calhoun

S, Escudero P, Tabain M, Warren P (eds.) Proceedings of the 19th International Congress of Phonetic Sciences. Australasian Speech Science and Technology Association Inc., Melbourne,

Australia, pp. 2149–2153 Google Scholar  * Lai C, Sui Y, Yuan J (2010) A corpus study of the prosody of polysyllabic words in Mandarin Chinese. Speech Prosody 2010 100457:1–4.

http://www.sprosig.org/sp2010/papers/100457.pdf. Accessed on 9 Jul 2023 * Liu S, Sóskuthy M (2022) Evaluating the accuracy of forced alignment across Mandarin varieties. The Journal of the

Acoustical Society of America 151:A131. https://doi.org/10.1121/10.0010882 Article  ADS  Google Scholar  * MacKenzie LTurton D (2020) Assessing the accuracy of existing forced alignment

software on varieties of British English. Linguist Vanguard 6(s1):20180061. https://doi.org/10.1515/lingvan-2018-0061 * Mahr TJ, Berisha V, Kawabata K, Liss J, Hustad KC (2021) Performance

of forced-alignment algorithms on children’s speech. Journal of speech, language, and hearing research JSLHR 64(6S):2213–2222. https://doi.org/10.1044/2020_JSLHR-20-00268 Article  PubMed 

PubMed Central  Google Scholar  * McAuliffe M, Socolof M, Mihuc S, Wagner M, Sonderegger, M (2017) Montreal Forced Aligner: trainable text-speech alignment using Kaldi. Proceedings of the

18th Conference of the International Speech Communication Association, 498–502. https://doi.org/10.21437/Interspeech.2017-1386 * McAuliffe M (2021) Update on Montreal Forced Aligner

performance. https://memcauliffe.com/update-on-montreal-forced-aligner-performance.html. Accessed 8 May 2022 * Pettarin A (2018) GitHub-pettarin/forced-alignment-tools: a collection of links

and notes on forced alignment tools. https://github.com/pettarin/forced-alignment-tools. Accessed 8 May 2022 * Sanker C, Babinski S, Burns R, Evans M, Johns J, Kim J, Smith S, Weber N,

Bowern C (2021) (Don't) try this at home! The effects of recording devices and software on phonetic analysis. Language 97(4):e360–e382. https://doi.org/10.1353/lan.2021.0075 Article 

Google Scholar  * Schiel F, Draxler C, Baumann A, Ellbogen T, Steffen A (2012) The production of speech corpora, version 2.5. https://epub.ub.uni-muenchen.de/13693/1/schiel_13693.pdf.

Accessed 5 Sept 2022 * Sella V (2018) Automatic phonological transcription using forced alignment: FAVE toolkit performance on four non-standard varieties of English. Bachelor’s degree

Project English Linguistics. Department of English. Stockholms University * Třísková H (2016) De-stressed words in Mandarin: drawing parallel with English. In: Hongyin Tao (eds.) Integrating

Chinese linguistic research and language teaching and learning. 121–144. John Benjamins Publishing Company. https://doi.org/10.1075/scld.7.07tri * Wagner M, Watson DG (2010) Experimental

and theoretical advances in prosody: a review. Lang Cogn Process 25(7-9):905–945 Article  PubMed  PubMed Central  Google Scholar  * Wu H, Yun J (2022) Prosodic disambiguation of

_Wh_-indeterminates in Mandarin Chinese. Manuscript * Yang L (2010) Putonghua shuangyinjie ci de zhongyin moshi yu cipin guanxi (The relationship between the stress pattern and word

frequency of disyllabic words in Mandarin). In: Clemens LaurenE, Liu Chi-MingLouis eds. Proceedings of the 22rd North American Conference on Chinese Linguistics (NACCL-22) and the 18th

International Conference on Chinese Linguistics (IACL-18). Harvard University, Cambridge, MA, pp. 274–282 Google Scholar  * Yuan J, Ryant N, Liberman M (2014) Automatic phonetic segmentation

in Mandarin Chinese: boundary models, glottal features and tone. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2539–2543,

https://doi.org/10.1109/ICASSP.2014.6854058 * Yuan J, Liberman MY (2015) Investigating consonant reduction in Mandarin Chinese with improved forced alignment. Proc Interspeech

2015:2675–2678. https://doi.org/10.21437/Interspeech.2015-401 Article  Google Scholar  * Yuan J, Lai W, Cieri C, Liberman MY (2018) Using forced alignment for phonetics research. In: Huang

C-R, Hsieh S-K, Jin p (eds) Chinese language resources and processing: text, speech and language technology. Springer. Springer.

http://languagelog.ldc.upenn.edu/myl/ForcedAlignment_Final_edited.pdf. Accessed on 15 Jun 2022 Google Scholar  Download references ACKNOWLEDGEMENTS This study is supported by the Faculty

Development Fund from the School of Modern Languages and the 2022 Summer Professional Development from Ivan Allen College of Liberal Arts, both awarded to the first author at Georgia

Institute of Technology. The authors are grateful to Hossep Dolatian and Jamilläh Rodriguez for their valuable comments and suggestions. The authors thank all participants who took part in

this study. The first author would also like to thank Zhaoran Ma for helping with recruiting participants, Alison Valk for helping with professional audio recording equipment setup and space

reservations. Any remaining errors are solely the responsibility of the authors. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Georgia Institute of Technology, Atlanta, GA, USA Hongchen Wu,

 Xiang Li, Huiyi Huang & Chuandong Liu * Stony Brook University, Stony Brook, NY, USA Jiwon Yun Authors * Hongchen Wu View author publications You can also search for this author

inPubMed Google Scholar * Jiwon Yun View author publications You can also search for this author inPubMed Google Scholar * Xiang Li View author publications You can also search for this

author inPubMed Google Scholar * Huiyi Huang View author publications You can also search for this author inPubMed Google Scholar * Chuandong Liu View author publications You can also search

for this author inPubMed Google Scholar CONTRIBUTIONS HW: conception and design of the study; funding acquisition; project administration; data collection, annotation, analysis, and

interpretation; drafting and revising the manuscript. JY: data analysis and interpretation; checking and revising the manuscript. XL: data annotation; forced aligner experimenting and

documentation; drafting the section for the format of transcripts and dictionaries. HH: data annotation; efficiency test documentation; drafting the tables for accuracy rates. CL: data

annotation; efficiency test documentation. All authors approved the final manuscript to be published. All authors agreed to be accountable for all aspects of the work in ensuring that

questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. CORRESPONDING AUTHOR Correspondence to Jiwon Yun. ETHICS DECLARATIONS

COMPETING INTERESTS The authors declare no competing interests. ETHICAL APPROVAL The questionnaire and methodology for this study were approved by the Institutional Review Board of Stony

Brook University (IRB2021-00358), and the Institutional Review Board of Georgia Institute of Technology (H21340) as minimal risk research qualified for exemption status. INFORMED CONSENT

Informed consent was obtained from all participants to contribute the audio files and have their recordings to be analyzed and presented in an anonymous way in an academic venue. All the

data from this study is saved on a password-protected share drive that only the study team has full access to. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with

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