Building a Kansai accent dictionary using YouTube
Hiroto Noguchi (Sophia University/Tokyo Medical and Dental University)

YouTube を利用した関西方言アクセント辞書の作成
野口大斗（上智大学／東京医科歯科大学）†
Abstract
This paper introduces an effort to create an accent dictionary of the Osaka dialect using usergenerated content on YouTube. Speech is extracted from videos, transcribed, and then forcedalignment is performed on the transcribed speech. The pitch of each segment is measured, and thepitch is automatically encoded. This paper reports the results of a preliminary application of theprocess to a single video.
1. Introduction
There has been a remarkable development in language processing, such as TTS and language
models. However, for languages with a small number of speakers, efforts to develop language
resources have lagged behind. The same is true within the Japanese language. While there is a certain
amount of linguistic resources for standard Japanese, this is not the case for dialects. In the case ofaccents, there are accent dictionaries for the Tokyo dialect, such as NHK (1998) and open-source ones
(Tachibana & Katayama, 2020). On the other hand, there are accent dictionaries for Osaka dialects(Sugito, 1995), but I cannot find any large open-source ones.
With that said, it is challenging to have tens of thousands of words read by several speakers
unless a researcher is blessed with informants and ample research funds. This paper presents anapproach to the automatic generation of accent dictionaries using data from YouTube, a user-generated
content platform.
2. Previous Studies
Sugito (1995) interviewed three speakers of each of the two generations for their accentpatterns. As shown in (1), the pitch of each word is indicated by L and H. Unlike Tokyo Japanese, theOsaka dialect differs from Tokyo Japanese in whether the pitch starts high or low, depending on theword.
(1)
a. kodomo
b. i’notic. kimi’rad. Suzumee. hata’ke‘child’‘life’‘you’‘sparrow’‘field’

HHH
HLL
HHL
LLL
LHL

The creation of accent dictionaries has so far been mainly based on fieldwork, using written,speech-listening, and recording methods. While the accents of words needed for headwords can beobtained efficiently, the problems lie in the fact that many of them are based on lists that are not natural† noguchih425@gmail.comspeech and that they are a time-consuming process.
3. Methods
3.1 Data
To eliminate the burden on the people surveyed and to obtain natural speech, data from
YouTube was used. In selecting YouTubers, several were chosen based on the following conditions:
the YouTuber must be from the Kansai region, must be speaking alone, and must have no backgroundmusic. A video of one of them was used in this study.
3.2 Procedures
The audio was separated from the video, and the utterances were transcribed using Whisper's(Radford et al., 2023) large model. For each utterance, a text file with the same name except for thefile extension was prepared with the utterance content written in hiragana, and segmentation wasperformed. The transcribed text was converted into hiragana using mecab-ipadic-NEologd(Toshinori, 2015). If a morphological analyzer supporting new words is not used, the new words will
not be recognized correctly. An example of segmentation results is shown in Figure 1.

Figure 1 An example of segmentation.
For each segment, the average pitch was measured in Hertz and assigned an H and L basedon whether it was higher or lower than the average for each word. Word alignment was performed
using mora counts of each word obtained during morphological analysis.
4. Results
For each segment in (2), the pitch was measured as in (3). To avoid downstep effects, theaverage pitch for each word was compared to the segment, resulting in the pitch pattern in (4).
Although there are some differences from the auditory impression, the pitch curve in Figure 2 can betranscribed to the symbols.
(2) ['kyo', 'u', 'ka', 'ra', 'yu:', 'chu:', 'bu', 'ha', 'ji', 'me', 'ma', 'shi', 'ta'](I started my YouTube channel today.)(3) [123.8923, 132.7712, 153.3251, 146.7026, 155.5783, 206.0359, 188.1586, 122.4416,116.2355, 116.8804, 110.4808, 108.3529, 139.1947](4)a. ['kyo', 'u']b. ['ka', 'ra']c. ['yu:', 'chu:', 'bu']d. ['ha', 'ji', 'me']e. ['ma', 'shi']f. ['ta']['L', 'H']['H', 'L']['L', 'H', 'H']['H', 'L', 'L']['H', 'L']['L']

“today”“from”“YouTube”“start”(POLITE)(PAST)

Figure 2 The pitch contour of (2).
5. Discussion
At first glance, such a pitch transcription may not seem appropriate. However, some words
appear more than once. For example, (5) shows which accent pattern the word YouTube was identified
within the video. The most frequently pronounced pattern, (5)a, which was pronounced ten times, isalso consistent with the auditory impression.

(5)['yu:', 'chu:', 'bu'] ['H', 'H', 'L'] * 10['yu:', 'chu:', 'bu'] ['L', 'H', 'L'] * 2['yu:', 'chu:', 'bu'] ['H', 'L', 'L'] * 1['yu:', 'chu:', 'bu'] ['L', 'H', 'H'] * 1
By increasing the number of videos, it is possible to obtain a more reliable accent pattern fora larger number of words. In this 16-minute video alone, 474 types and 918 tokens appeared in termsof word count. Further investigation is needed in the future.
Acknowledgements
I would like to thank the YouTube Research Program for permission to use the data used inthis paper.
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