Parallel Speech Corpora of Japanese Dialects
Koichiro Yoshino1 , Naoki Hirayama2,† , Shinsuke Mori3 ,Fumihiko Takahashi4,† , Katsutoshi Itoyama5 , and Hiroshi G. Okuno5,6
1

Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, 630-0192, Japan
2
Industrial ICT Solutions Company, Toshiba Corporation, 3-22, Katamachi, Fuchu, 183-8512, Japan
3
Academic Center for Computing and Media Studies, Kyoto University, Sakyo, Kyoto, 606-8501, Japan
4
Yahoo Japan Corporation, Midtown Tower, 9-7-1 Akasaka, Minato-ku, Tokyo, 107-6211, Japan
5
Graduate School of Informatics, Kyoto University, Sakyo, Kyoto, 606-8501, Japan
6
School of Creative Science and Engineering, Waseda University, Ohkubo, Shinjyuku, Tokyo 169-0072, Japankoichiro@is.naist.jp, naoki2.hirayama@toshiba.co.jp, forest@i.kyoto-u.ac.jp,
ftakahas@yahoo-corp.jp, itoyama@kuis.kyoto-u.ac.jp, okuno@aoni.waseda.jp
Abstract
Clean speech data is necessary for spoken language processing, however, there is no public Japanese dialect corpus collected for speechprocessing. Parallel speech corpora of dialect are also important because real dialect affects each other, however, the existing data only
includes noisy speech data of dialects and their translation in common language. In this paper, we collected parallel speech corpora ofJapanese dialect, 100 read speeches utterance of 25 dialect speakers and their transcriptions of phoneme. We recorded speeches of 5common language speakers and 20 dialect speakers from 4 areas, 5 speakers from 1 area, respectively. Each dialect speaker converted
the same common language texts to their dialect and read them. Speeches are recorded with closed-talk microphone, using for spoken
language processing (recognition, synthesis, pronounce estimation). In the experiments, accuracies of automatic speech recognition(ASR) and Kana Kanji conversion (KKC) system are improved by adapting the system with the data.
Keywords: Speech, Transcription, Dialect, Japanese

1.

Introduction

speech processing.

Texts and their read speeches are necessary for natural andspoken language processing (NLP and SLP). Tireless efforts of data collection of speech data and their transcriptions from the early stage of NLP and SLP researches drastically improved accuracies of a variety of NLP and SLP
tasks. However, we still do not have enough resources forsome minor languages, and it causes less accuracies in minor languages (Kominek and Black, 2006).
Especially, we do not have enough speech data of dialects.
The lack of dialect speech data disturbs the use of NLP and
SLP applications of dialect speakers. For example, it is very
difficult to recognize dialect speeches accurately by usinga model trained in common language. Automatic speech
recognition (ASR) system is now generally used in public services, for example, taking minutes of national congresses and city councils (Akita et al., 2009). Such applications are expected to be used for not only current formal situations but also more casual situations, for example,
taking minutes of courts, and dialect speech recognition isnecessary for this purpose.
If we have a small parallel set of dialect and common language, SLP technologies developed in common language
can be applied to the dialect rapidly. Japanese dialectspeeches are collected and transcribed in linguistics area
(National Institute for Japanese Language and Linguistics,
2001 2008), however, the sound data of the corpus is notcollected for using on speech processing. It is recordedon analog recording, and the recording situation is not
close talk. Even worse, the corpus does not have parallel
data of common language in speech, which is important in†This work was done when they were at Kyoto University.

Furthermore, rapid progression of globalization blurboundaries between dialects. It is not rare to live in several areas in the process of growing, and the speaking styleof such person is affected by several dialects. To process
such mixed dialect speech, it is necessary to construct parallel corpora between not only common language and dialect but also dialect and dialect. Such corpora realize tobuild a system that assumes mixed dialect as an input.
In this work, we collected parallel speech data of common
language and several dialects in Japanese. We recorded
reading speech of 100 Japanese sentences of balanced texts
on their vocabulary, which is read by 5 common language(= Tokyo dialect in Japanese) speakers. The common language text consists of segmented words and their pronunciations. We requested several dialect speakers to convert
the text to their dialect in the same meaning. They read thetext in recording, and annotators transcribed their pronunciations. We prepared 20 dialect speakers from 4 areas, there
are 5 speakers from 1 area, respectively. Thus, the resultant
resources are parallel corpora of 1 common language and4 dialects with their read speeches and transcriptions (texts
and pronunciations).
We evaluated the collected data in automatic speech recognition (ASR) and Kana Kanji conversion (KKC) (Mori etal., 1999; Takahashi and Mori, 2015) systems. For the
ASR evaluation, we constructed dialect speech recognizers
that uses machine translation techniques to work on dialectspeeches (Hirayama et al., 2015). The ASR accuracies ofdialect speeches are improved by adapting recognizers byusing the speech and transcription resources based on basic
adaptation methods of ASR system. For the KKC evaluation, we constructed dialect Kana Kanji conversion sys-4652

tems. The KKC accuracies of dialect are also improved byadapting converters by using the constructed language resources.

2. Related Works
There are some dictionary based approaches for dialectspeech processing (Brinton and Fee, 2001; Thomas, 2004;
Ramon, 2006; Woods, 1979). The dictionary based approach requires a specialist who analyzes the dialect, andthe cost of dictionary construction is very high. Recently,
corpus based statistical approaches are popularly used inspeech processing. It realizes the system construction if wehave a minimal corpus of a dialect.

TohokuSan-yoKansai2.1. Dialect Language Resource in Japanese
The state of the art dialect language resource is publishedby (National Institute for Japanese Language and Linguistics, 2001 2008). The corpora consist of 20 volumes,
records 48 dialects that are spoken by pure dialect speakers (mainly elder people). Each recording has more than
20 minutes and two speakers. The corpora also have transcription in Katakana character and translation in common
language (Tokyo dialect) for each speech. The corpora preserve pure Japanese dialect, however, the recording setting
is not suitable for spoken language processing (noisy, notclosed talk).
The lack of dialect data is also a problem of NLP. Collecting large scale text data becomes easier by the buildup ofWeb, however, it is still difficult to collect large scale dialect
data from Web. We tackled this problem with a conversionof common language resources on Web to dialects basedon machine translation techniques (Hirayama et al., 2012).
We trained Weighted Finite State Transducer (WFST) thatconverts common language sentences to dialect pronunciation sequences by using a small parallel corpus of common language in Japanese and Kansai dialect (dialect spoken in Kansai area in Japan) extracted from the database of(National Institute for Japanese Language and Linguistics,
2001 2008). The trained WFST generates large scale simulated dialect corpus to be used for the training of NLP and
SLP applications.

KyushuFigure 1: Locations of areas where dialects are spoken.
parallel corpora. The location of each area is shown in Figure 1. These areas are selected to cover major dialects spoken in Japan. Hokkaido (the northernmost area in Figure 1)
and Okinawa (south islands depicted on lower right of Figure 1) are not covered by the following reasons.
• Hokkaido is exploited after the Meiji restoration in1868 by people from various region, thus, the major
dialect spoken in Hokkaido is common language.
• Okinawa has different building history from other areas, and the dialect is greatly different from the common language.

3.1. Recording Procedure
The recording procedure of proposed dialect corpora consists of following four steps.

2.2. ASR system in Japanese Language
One of the state-of-the-art ASR system in Japanese is Julius
decoder (Lee et al., 2001)1 . Julius packages deep neuralnetwork based acoustic model and language model trained
from Balanced Corpus of Contemporary Written Japanese(BCCWJ) (Maekawa, 2008)2 . The language model supports to recognize Japanese common language utterances,
however, it does not support any dialect speech. The language model is easily updated by adding a training text
data, and we tried to update the language model by adding
simulated dialect text generated from the WFST based converter (Hirayama et al., 2012).

3. Parallel Dialect Corpora
We selected one common language (Tokyo dialect) and four
dialects (Kansai, Kyushu, Tohoku, and San-yo) to construct
1
2

Tokyo

1. We randomly selected 100 sentences of common language to be read by common language and dialect
speakers. These sentences are selected from BCCWJ
blog category. Honorific expressions are normalized
to plain expressions, to make expressing speaker’s dialect easier.
2. Dialect speakers convert the selected 100 sentences
into their own dialect sentences. We have 5 speakers
for each dialect, and each speaker of the same dialect
converts sentences by themselves. In other words, wehave 5 different transcriptions for each dialect, because expression details depend on the backgrounds of
speakers even if their dialect categories are the same.
This step is skipped if the speaker is common language(Tokyo dialect) speaker.
3. Speakers read their own converted sentences. Common language speakers read the produced sentences.
Speeches are recorded by a close talking microphone.
4. Pronunciations are transcribed into phonemes. We
used phoneme set defined in Japanese Newspaper Ar-

http://julius.osdn.jp/
http://pj.ninjal.ac.jp/corpus center/bccwj/

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Table 3: Number of phonemes.
#1
#2
#3
#4
Tokyo
5701
Kansai 5,525 5,582 5,603 5,687
Kyushu 5,629 5,848 5,555 5,727
Tohoku 5,580 5,813 5,512 5,566
San-yo 5,478 5,481 5,624 5,507

Table 1: Age and gender of speakers (M: males and F: females). Nos. 1–5 correspond to indices of speakers of five
dialects.
#1
#2
#3
#4
#5
Tokyo
38, F 36, M 32, M 25, M 21, F
Kansai30, F 27, M 24, F 23, M 20, F
Kyushu 28, M 24, F 22, M 20, F 40, M
Tohoku 26, M 24, F
21, F
20, F 26, M
San-yo 49, F 24, M 22, M 21, F 21, M

#5
5,486
5,721
5,539
5,485

F-Score92.50%

Table 2: Recording time of each speaker. Nos. 1–5 correspond to indices of speakers of five dialects.
#1
#2
#3
#4
#5
Tokyo 9:21 8:17 9:40 8:39 9:24
Kansai 9:07 8:06 8:09 7:57 8:09
Kyushu 6:29 8:22 6:53 7:42 8:14
Tohoku 7:05 8:35 8:19 10:24 7:59
San-yo 7:54 8:43 8:02 7:55 7:56

92.00%
91.50%
91.00%
90.50%
90.00%
89.50%
Added
sentences

89.00%
0ticle Sentences (JNAS) corpus (Acoustical Society ofJapan, 1997)3 .

3.2. Corpus Specifications
The age and the gender of each speaker are summarized inTable 1. Every speaker lived in the area of the dialect until
18 year old. Note that, we did not limit the other conditions. For example, we did not care about the living areafter 18 year old, thus, subjects are affected by several dialect that they touched. They also must be affected by TV
broadcasting in common language. There are so many factors that may affect the speaking style of each speaker. Table 2 shows total recording time of each speaker. We did
not control the speaking speed. Table 3 shows number of
total phonemes in pronunciations of each speaker.

3.3. Example of Corpora
We show an example text set of the proposed corpora in
Table 4. The original texts is Tokyo, which came from BCCWJ corpus. The original BCCWJ includes annotationsof word segment and pronunciation. After the recording,phonemes are transcribed. Pronunciation of each dialect isconverted from the transcribed phoneme. Translation examples of other 4 areas are shown in other lines (Kansai,
Kyusyu, Tohoku and San-yo). The meanings of sentences
are the same, however, there are small differences in minor
word choices. For example, “i t e” (there is) in Tokyo ischanged as “o tt e” in Kansai, Kyusyu, and San-yo. This is
one of a typical change of pronunciation in the west-area ofJapan.

4. Applications
4.1. Evaluation in ASR System
We evaluated the collected corpus in ASR systems. Figure 5 shows ASR accuracies of each speaker recognized by
a common language ASR system. The language model istrained in Yahoo! Q&A corpus (over 3 million sentences)

100

150

200

Kyushu250

300

Tohoku

350

400

450

San-yoFigure 2: F score of KKC and the added number of dialect
sentences to the training set.
(Yahoo Japan Corporation, 2007)4 , and the acoustic modelis trained in Corpus of Spontaneous Japanese (CSJ) corpus(National Institute for Japanese Language and Linguistics,
2006)5 and Japanese Newspaper Article Sentences (JNAS)
corpus, respectively. We used Julius decoder.
As shown in the Table 5, the ASR performances are drastically decreased in dialects even if the system works wellin common language (= Tokyo dialect), and this result corroborates the importance of maintenance of dialect speech
resources. Table 6 shows accuracies of ASR with models
adapted by the adaptation technique proposed in our previous work (Hirayama et al., 2015). The accuracies are
increased more than 10% in total, and these results verify
the importance and usefulness of our parallel dialect speech
data.

4.2. Evaluation in KKC System
Kana Kanji conversion (KKC) is a task of convertinga given pronunciation sequence to a Kana and Kanji
sequence, which is generally used in input method ofJapanese. We constructed a statistical Kana Kanji converter(Mori et al., 1999; Takahashi and Mori, 2015) by using BCCWJ corpus as a training set. We picked up 50 utterancesfrom each dialect (50×5) in the constructed dialect corpus
as testing sets, and used lest 450×5 sentences as additional
training sets. We evaluated the F score (harmonic mean of
precision and recall) of KKC of the testing sets. Figure 2
shows that the F scores of KKC depend on the added numbers of dialect sentences to the training set. 0 in horizontal axis means the baseline converter as is, and 450 means
fully adapted converter by adding dialect sentences to the
4350
Kansai5

http://research.nii.ac.jp/src/4654

http://www.nii.ac.jp/dsc/idr/yahoo/chiebkr2/Y chiebukuro.html
http://pj.ninjal.ac.jp/corpus center/csj/

Area
Tokyo

Speaker

Kansai#1

Kyusyu#1

Tohoku#1San-yo#1(cf. Translation)

Table 4: Examples of transcribed corpora.
Texts (word/pronunciation)
Transcribed phonemes
アニメ/あにめ に/に は/わ 作画/さくがanimeniwasakuga
監督/かんとく と/と いう/いう 人/ひと
kaNtokutoiuhito
が/が い/い て/て 、/NA 毎回/まいかいg a i t e sp m a i k a i
話/はなし ごと/ごと に/に 絵/え が/が
h a n a sh i g o t o n i e g a
違う/ちがう の/の は/わ 、/NA その/そのch i g a u n o w a sp s o n o
監督/かんとく さん/さん が/が 前回/ぜんかいkaNtokusaNgazeNkaiと/と 違う/ちがう 人/ひと だ/だ から/から
t o ch i g a u h i t o d a k a r a
だ/だ そう/そー だ/だ 。/NAd a s o: d a sp silE
アニメ/あにめ に/に は/わ 作画/さくがanimeniwasakuga
監督/かんとく って/って 人/ひと
kaNtokuqtehito
が/が おっ/おっ て/て 、/NA 毎回/まいかいg a o q t e sp m a i k a i
話/はなし ごと/ごと に/に 絵ー/えー
h a n a sh i g o t o n i e:
違う/ちがう ん/ん は/わ 、/NA その/そのch i g a u N w a sp s o n o
監督/かんとく さん/さん が/が 前回/ぜんかいkaNtokusaNgazeNkaiと/と 違う/ちがう 人/ひと や/や から/から
t o ch i g a u h i t o y a k a r a
や/や ねん/ねん て/て 。/NAy a n e N t e sp silE
アニメ/あにめ に/に は/わ 作画/さくがanimeniwasakuga
監督/かんとく って/って いう/ゆー 人/ひとk a N t o k u q t e y u: h i t o
が/が おっ/おっ て/て 、/NA 毎回/まいかいg a o q t e sp m a i k a i
話/はなし ごと/ごと に/に 絵/え が/が
h a n a sh i g o t o n i e g a
ちごう/ちごう とっと/とっと は/わ 、/NA その/そのch i g o u t o q t o w a sp s o n o
監督/かんとく さん/さん が/が 前回/ぜんかいkaNtokusaNgazeNkaiと/と ちがう/ちがう 人/ひと だ/だ から/から
t o ch i g a u h i t o d a k a r a
そう/そー だ/だ 。/NAs o: d a sp silE
アニメ/あにめ に/に は/わ 作画/さくがanimeniwasakuga
監督/かんとく と/と いう/いう 人/ひと
kaNtokutoiuhito
が/が い/い で/で 、/NA 毎回/まいかいg a i d e sp m a i k a i
話/はなし ごと/ごと に/に 絵/え が/が
h a n a sh i g o t o n i e g a
違う/ちがう の/の は/わ 、/NA その/そのch i g a u n o w a sp s o n o
監督/かんとく さん/さん が/が 前回/ぜんかいkaNtokusaNgazeNkaiと/と 違う/ちがう 人/ひと だ/だ はん/はん で/で だ/だ t o ch i g a u h i t o d a h a N d e d a
そう/そー だ/だ 。/NAs o: d a sp silE
アニメ/あにめ に/に は/わ 作画/さくがanimeniwasakuga
監督/かんとく と/と ゆう/ゆー 人/ひとk a N t o k u t o y u: h i t o
が/が おっ/おっ て/て 、/NA 毎回/まいかいg a o q t e sp m a i k a i
話/はなし ごと/ごと に/に 絵/え が/が
h a n a sh i g o t o n i e g a
違う/ちがう ん/ん は/わ 、/NA その/そのch i g a u N w a sp s o n o
監督/かんとく さん/さん が/が 前回/ぜんかいkaNtokusaNgazeNkaiと/と 違う/ちがう 人/ひと だ/だ から/から じゃ/じゃt o ch i g a u h i t o d a k a r a j a
けえ/けえ と/と 。/NAk e e t o sp silE
There is a job named animation director who direct drawing of animation, and the reason ofeach animation episode has different characteristic drawing is that each animation episode has
a different animation director.

training set. This result shows that the constructed dialect
resource increases the accuracy of Kana Kanji conversion,and it is also useful for applications of NLP.

4.3. Other Possible Applications
There are some possible applications improved by the proposed dialect data in this paper. Text to speech (TTS) includes some modules that require dataset for the adaptation(Nagano et al., 2005), pronunciation estimation or F0 estimation, and it is one of the most major area that requires
parallel speech data. Statistical machine translation (Brown

et al., 1993) is also an application that requires parallel dataof texts and speeches. The machine translation between thecommon language and dialect intermediates in conversion
of systems for common language to systems for dialect.
Parallel corpora of several dialects benefit who constructs
a system that assumes mixed dialect as an input, and it
also realizes to estimate the proportion of affected dialectof the user (Hirayama et al., 2015). This technology can beapplied for other types properties that affect the speaking
style of people, for example, jobs or hobbies. This work indicates the importance of the construction of such parallel

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Table 5: Accuracies of ASR system trained in common language.
#1
#2
#3
#4
#5
Tokyo 84.7 78.1 84.7 82.4 80.0
Kansai 51.6 49.4 61.2 50.9 50.1
Kyushu 44.6 46.0 41.2 57.5 50.4
Tohoku 44.5 33.0 28.9 33.3 58.8
San-yo 66.1 65.5 51.7 54.4 66.3
Table 6: Accuracies of ASR system adapted to the target
dialect.
#1
#2
#3
#4
#5
Kansai 61.4 60.1 67.3 60.3 60.0
Kyushu 49.4 57.5 47.2 66.6 59.9
Tohoku 49.7 42.7 37.9 42.8 67.9
San-yo 81.8 76.1 65.2 66.0 76.1

conversion, and these applications are improved by the language resource. The constructed parallel speech corpora
consist of common language and several dialects, thus, it
can be used to model the difference between not only common language and a dialect but also between a dialect andanother dialect. It will benefit a variety of tasks in spoken
language processing and natural language processing.

7.

Acknowledgment

The corpus is created in the project of JSPS KAKENHI
Grant No.24220006. This work is also supported by JSPS
KAKENHI Grant No.15660505.

8.

Use of Corpora

There is no restriction of the usage of the corpora. We request you to cite this paper and the project of JSPS KAKENHI Grant No.24220006 when you publish anything thatuses this resource. The resource is provided on the project
web page7 .

corpora.

9. Bibliographical References

5. Discussion for Annotation
There are some discussions for annotation of the collected
corpora. Word segments, accents, and the phoneme set are
the points it should be updated in future.

5.1. Word Segment
The original texts from BCCWJ (= common language)
have word segmentation annotations. The translated dialect
texts are automatically segmented by KyTea (Neubig et al.,2011)6 . We plan to annotate the word segments of dialectsthat can be aligned to the original text of common language,
however, we still need discussion for annotation standard ofword segmentation of dialects.

5.2. Accent
Accents are characteristics of dialect speech in Japanese.
This information will benefit to use the corpora in speechsynthesis area (Nagano et al., 2005).

5.3. Phoneme Set
In the first version of the corpora, we annotated phonemes
with in the phoneme set defined in JNAS. However, JNAS
is a common language speech corpus and the phoneme set
has some mismatches to real dialect speeches. We need toextend the phoneme set in future version.

5.4. Superposition with Other Annotations
BCCWJ is one of the most popular Japanese language resource, and some previous works tries to annotate several types of annotations: dependency trees (Mori et al.,2014) and predicate argument structures (Komachi and
Iida, 2011). We can superpose these annotations with thedialect corpora, to use more higher NLP layers.

6. Conclusion
We constructed parallel speech corpora of common language and several dialects in Japanese. The parallel corpus is used for adapting speech recognition and Kana Kanji
6

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