CONSTRUCTION OF A LARGE-SCALE JAPANESE ASR CORPUS ON TV RECORDINGS
Shintaro Ando1,2 , Hiromasa Fujihara1
1

arXiv:2103.14736v1 [cs.SD] 26 Mar 2021

2

Laboro.AI, Inc., Tokyo, Japan
Graduate School of Engineering, The University of Tokyo, Tokyo, Japan{ando,fujihara}@laboro.ai
ABSTRACTThis paper presents a new large-scale Japanese speech corpus fortraining automatic speech recognition (ASR) systems. This corpus contains over 2,000 hours of speech with transcripts built on
Japanese TV recordings and their subtitles. We develop herein an
iterative workflow to extract matching audio and subtitle segmentsfrom TV recordings based on a conventional method for lightlysupervised audio-to-text alignment. We evaluate a model trainedwith our corpus using an evaluation dataset built on Japanese TEDx
presentation videos and confirm that the performance is better thanthat trained with the Corpus of Spontaneous Japanese (CSJ). The experiment results show the usefulness of our corpus for training ASR
systems. This corpus is made public for the research community
along with Kaldi scripts for training the models reported in this paper.
Index Terms— Automatic speech recognition, Corpus
1. INTRODUCTION
Deep learning-based automatic speech recognition (ASR) systems
heavily rely on the quantity of training data. Many studies today,
especially those for commercial purposes, use over 5000 hours ofspeech data [1–3]. However, the amount of publicly available speechcorpora is insufficient for languages other than English. While it
is common in English ASR research to use relatively large corpora
such as the SwitchBoard-Fisher dataset and LibriSpeech [4], eachcontaining 2,000 and 960 hours of speech, respectively, the sizesof the commonly-used Japanese speech corpora are much smaller.
For example, two of the most popular corpora in Japanese ASR research, namely the Corpus of Spontaneous Japanese (CSJ) [5] andthe Japanese Newspaper Article Sentences (JNAS) corpus [6], only
contain approximately 600 and 90 hours of speech, respectively.
Although an increasing demand for large-scale speech corpora
for training high-quality ASR systems has been observed, constructing them is not straightforward because it usually requires
massive manual labor of transcription or recording. Many studies
have been conducted to collect data without such laborious manual
works. One approach to solving the problem is to collect data in
a community-driven manner, as done in the VoxForge [7] and theCommon Voice [8] project. Another common solution is to takeadvantage of the existing data and format them as a speech corpus.
For example, the aforementioned LibriSpeech [4] corpus was built
from publicly available audiobooks. Building an ASR corpus usingsocial media videos with manual subtitles, which is less certain astranscripts, has also been investigated. The TED-LIUM corpus [9],containing 450 hours of speech, was built from TED conference
We thank NTT PC Communications Inc. and their Innovation LAB program for letting us use their GPU resources.

videos. Soltau et al. [10] extracted more than 100,000 hours of
training data from YouTube videos, although they are not publicly
available.
Broadcast TV programs with subtitles can be another sourceof speech corpora, and their usage has been investigated. The
timestamps of TV subtitles are often inaccurate; hence, several
studies have been made to correct them. The Alignment task of
the Multi-Genre Broadcast (MGB) Challenges [11, 12] required researchers to train acoustic models from TV recordings by providingthem with English and Arabic TV recordings. Several researchteams explored the effect of forced alignment and lightly-supervised
decoding [12–15]. Bang et al. [16] built a Korean ASR corpus
containing 336 hours of speech from a Korean TV broadcast, where
the authors focused on utilizing the original timestamps and refining
them effectively.
Following the studies that utilized broadcast TV programs, thispaper introduces LaboroTVSpeech, a new large-scale Japanese ASR
corpus built on Japanese TV recordings and their subtitles. The current release contains approximately 2,000 hours of speech extracted
from 8 months of recording period. The process of corpus construction is fully automated; thus, the amount of data available will increase as time goes by.
The main contributions of this paper are as follows: (1) We established an automated workflow for constructing an ASR corpus
from a Japanese TV broadcast with inaccurate subtitles. (2) We
developed a new Japanese speech corpus, called LaboroTVSpeech,
which contains 2,000 hours of speech, and made it publicly available
for the research community1 . To the best of our knowledge, there
is no publicly-available Japanese corpus of this scale. (3) We alsoestablished an evaluation dataset for ASR systems, called TEDxJP10K, which is independent from the other ASR corpora and lets us
fairly evaluate the performance of the ASR corpora.
The rest of the paper is organized as follows. Section 2 elaborates on the corpus construction procedure from the original TV
recordings and their subtitles. Section 3 describes the statistical details of the corpus. Section 4 presents the usefulness of the corpus
through ASR experiments; and Section 5 concludes the paper.
2. CORPUS CONSTRUCTION PROCEDURE
2.1. Data Collection and Preprocessing
The original audio data, their subtitles, and basic information suchas the title and genre of each TV program, were collected from 1seg
broadcast, which is a Japanese terrestrial digital audio/video and databroadcasting service for mobile devices. 1seg broadcast signals are
not protected by access control and legally accessible by any receivers, as opposed to ordinary broadcast TV signals that requires
1 https://github.com/laboroai/LaboroTVSpeechRaw data(Audio+Subtitles)

Step.1

Step.2

ASR-basedalignment

ASR-basedcleansing

Aligned data
(Audio+Transcripts)

Step.3

Step.4

Align with raw
subtitles

Non-aligned data
(Audio+Subtitles)

ASR-basedcleansing

Aligned data
(Audio+Transcripts)

Step.5
Validated data

Merge

(Audio+Transcripts)

Final corpus
Merged data
Repeat(Audio+Transcripts)

Fig. 1: Diagram of our framework.
a special card named B-CAS containing the cryptography key to decode and are only accessible to authorized receivers. 1seg broadcast
contains basically same programs with ordinary broadcast signals
but with a lower bit rate, although their difference is negligible whendownsampled to 16 kHz. The current corpus includes recordings
over the period of February–September 2020. We ignored TV programs that are reruns of already-recorded ones and those without
subtitles.
We recorded 9,142 TV programs of various lengths. Some information on the details of the original recording is further reported
in Section 3. Audio signals were decompressed and downsampled
to 16 kHz. Each subtitle segment was tokenized into a sequenceof words using MeCab [17] with mecab-ipadic-NEologd [18] as adictionary. MeCab does not correctly tokenize numbers with morethan a digit written in Arabic numerals; hence, we converted Arabic
numerals to Chinese characters and re-inferred their pronunciations,
if possible. We also added several Japanese-style pronunciations ofEnglish words to our dictionary using the dictionary file released in
the Bilingual Emacspeak Project [19].
2.2. Audio and Subtitle Alignment
TV programs and their subtitles have the following hallmarks, whichmake both subtitle texts and timestamps unreliable.
• The subtitle timestamps are not always on time. For programswith real-time subtitles, such as news reports, they could havetens of seconds of delay. Even non-realtime subtitles could
deviate from actual utterances for a few seconds.
• Even when programs themselves have subtitles, some audio
segments (i.e., commercials) do not have them.
• Subtitles are not always verbatim; words may be formatted ina simpler form for readability. In the Japanese language, spoken words and their formatted written version can be largely
different.
• In other cases, some parts of words in utterances are shownin the screen as images and excluded in subtitle texts. In
such cases, if you listen to audio and read subtitles without
watching videos, it looks as though several words are dropped
in subtitle sentences. This often appears in Japanese variety
shows.
The participants of Alignment task of the MGB challenge [13–15]
discarded the temporal information of subtitles and used the socalled lightly-supervised approach. After applying speech recognition to audio using a biased language model (LM), which is alanguage model adapted to subtitle texts, output transcripts and subtitle texts are forced-aligned to select matching segments. However,when we simply employed the lightly-supervised method as done inprevious studies [9, 15] in our task, the average extraction rate was
not as high as we desired. The average word extraction rate of all
programs was 68%, which further decreased to 63% when overly
short segments of less than a second were removed. This was only
45% in variety shows after the removal. We presumed that this was
mainly due to the several aforementioned peculiar characteristics
of Japanese TV programs when compared to audiobooks or TV
programs used in the previous studies.
The authors in [16] tried to utilize and refine the subtitle timestamps by concatenating the adjacent subtitles, expanding the temporal boundaries forward and backward for a few seconds, and applying the forced alignment. However, this approach cannot deal withrealtime subtitles, in which the deviation could be far more than afew seconds.
Our approach is generally based on the lightly-supervised decoding approach. We specifically adopted the scripts provided in the
Kaldi ASR toolkit [20]2 , which is based on the method describedin [15]. We proposed the iterative alignment of the remaining unmatched segments to extract the segments missed in the previousalignment attempts to overcome the low extraction rate issue. Our
approach consisted of the following five steps as shown in Figure 1.
Step 1 For each recorded program, the entire audio and the corresponding concatenated subtitles were aligned using the resultof the ASR with a biased LM trained with subtitle texts. We
first decoded the audio to obtain a text transcript with timestamps using a speech recognizer. We then aligned them withthe original subtitle texts using the Smith–Waterman alignment. segment_long_utterances_nnet3.sh scriptof Kaldi was used here.
Step 2 The ASR with another biased LM trained only with subtitle texts corresponding to the segment was applied for eachsegment extracted in the previous step. The invalid portionsof the transcript of each segment were then removed basedon the recognition results. clean_and_segment_data_
nnet3.sh script of Kaldi was used here. We then removed
the segments with transcripts having less than 10 words. The
outputs of this step contained decoded transcripts, not subtitle
texts.
Step 3 We obtained segments with timestamps and corresponding
subtitle texts by aligning the decoded transcripts of the extracted segments with the original subtitle. By this step, we
can also extract segments that were not aligned. We used theSequenceMatcher module in Python’s difflib package for the text alignment. We executed the alignment pro2 https://github.com/kaldi-asr/kaldi/blob/master/egs/wsj/s5/steps/cleanup/{segment long utterances nnet3.sh,clean and segment data nnet3.sh}Table 1: Statistics of the raw recordings and the extracted corpus. The numbers shown in the parentheses in the audio lengths denote thepercentages of each genre accounted for in the final extracted data.
Audio length (hours)#Words in trascripts
Genre
Raw
Extracted
Raw
Extracted Extraction rate (%)
News, report1800.0767.0 ( 37.4 %)9,380 K8,436 K
89.9
Variety show
1382.9316.4 ( 15.4 %)6,330 K3,368 K
53.2
Information/tabloid show
820.8
323.3 ( 15.8 %)4,812 K3,604 K
74.9
Drama
664.0
206.0 ( 10.1 %)3,175 K
2,304 K
72.6
Documentary/culture
462.4
175.6 (
8.6 %)
2,158 K
1,780 K
82.5
Hobby/education
304.1
101.0 (
4.9 %)
1,613 K
1,092 K
67.7
Sports
223.9
66.6 (
3.2 %)
1,140 K
726 K
63.7
Animation/special effect movies
175.2
39.7 (
1.9 %)
801 K
430 K
53.7
Music
99.2
17.6 (
0.9 %)
281 K
184 K
65.6
Welfare58.7
20.1 (
1.0 %)
286 K
212 K
74.0
Movies
28.4
6.2 (0.3 %)
121 K
72 K
59.4
Theatre/public performance
23.5
10.4 (
0.5 %)
150 K
118 K
78.5
Total
6042.9 2049.9 ( 100.0 %) 30,253 K 22,331 K
73.8
Table 2: Data subsets in LaboroTVSpeech.
train
dev
Audio length (hours)
2036.2
13.7
1.6 M
12 K
# Audio segments# Words
22 M 147 K
cess in both forward and backward directions and treated thetokens aligned with exactly the same token in both directions
as validly aligned tokens.
Step 4 For the segments that are not aligned in the previous step,
we executed the same ASR-based data cleansing as in Step 2.
We did not remove segments based on the number of words
in this step.
Step 5 We merged the segments extracted by steps 3 and 4. We
then passed the resultant dataset to Step 3 as “Aligned data”
in Figure 1, and repeated steps 3–5.
We expected that the repetition of steps 3–5 would allow us toachieve more data, especially those relatively difficult to align correctly in the first two steps, and contribute to the acoustic and linguistic variety of the corpus. In our current implementation we set
the maximum number of repetitions to 2. After the repetitions, we
passed the “Merged data” from the last Step 5 to Step 3 and obtained
the final validated dataset. We removed the segments shorter than asecond to ensure that all segments had a certain audio length for the
acoustic model training.
For the abovementioned decoding steps, we used Kaldi’s nnet3
acoustic model (AM) with a TDNN-chain architecture trained on the
CSJ corpus, containing approximately 520 hours of academic speech
dialogs. The model was built using Kaldi’s official CSJ recipe 3 .
3. DETAILS OF THE CORPUS
3.1. Statistics
Table 1 shows the statistics of the raw recordings and the extracted
corpus. For simplicity, we only used the first genre tag obtained fromeach recording, even though some programs belonged to multiple
genres. The word coverage rate was calculated as (total #words inthe extracted segments) / (total #words in the original subtitles). We
did not calculate the coverage rates in terms of the audio lengths
because they were influenced by factors, such as the total lengths ofcommercials in a program.
3 https://github.com/kaldi-asr/kaldi/tree/master/egs/csj/s5

The extraction process worked properly because the coverage
rates were higher than 50% in all genres. More precisely, the word
extraction rate largely varied between the genres from the lowest
53.2 % in variety shows to the highest 89.9 % in news reports. Thisvariation between genres seems to be caused by the difference ofquality of audio signals (i.e the level of background noises, spontaneousness, etc.) A noticeable deviation in the audio lengths between
the genres in the final corpus was found due to such differences andthe inequal total lengths of the raw recordings of each genre. Nevertheless, the corpus contained speeches of a large variety of speakers
and acoustic environments, which is beneficial to building an ASR
system.
3.2. Released Data
All audio samples were segmented based on the boundaries of theoriginal subtitles, with sampling rates of 16 kHz. The order of all
segments was randomly shuffled, regardless of their original TV
programs to prevent the corpus from being listened to as a TV program. The transcripts were in the form of a sequence of tokenizedwords. Each word contained a simple morpheme tag such as “noun”
or “verb,” obtained through preprocessing the original subtitles with
MeCab.
We split the whole dataset into training and development sets
(Table 2). We randomly selected 1,000 speech segments from eachgenre listed in Table 1 and defined the resultant dataset of 13.7 hoursof speech as the development data. The training data comprised theremaining dataset of 2036.2 hours. We did not provide a test set inthis corpus because it was nearly impossible not to make speakers in
the test set appear in the training or development set considering thatmany celebrities appear in several programs, and we cannot obtain
the exact information of the casts of each program. Instead of thetest set of LaboroTVSpeech, we provided TEDxJP-10K, which is adifferent dataset built using Japanese TEDx videos. The details of
the dataset is further described in 4.1.1.
4. AUTOMATIC SPEECH RECOGNITION EXPERIMENTS
4.1. Experimental Setup
We evaluated our corpus through two speech recognition tasks using two evaluation datasets: the TEDxJP-10K and CSJ evaluation
datasets, (i.e., eval 1–3). TEDxJP-10k is a dataset we built to evaluate ASR corpora, which is further described in 4.1.1. We report thecharacter error rate (CER) in each task using all words in the reference and hypothesized transcripts, including fillers and other words
caused by disfluencies.
4.1.1. The TEDxJP-10K dataset
The TEDxJP-10k dataset was built to compare the performances of
the Japanese ASR systems under a more realistic situation with diversed recording conditions, dialects and background noises, etc. In
many machine learning datasets, the test sets are developed as subsets of the entire data, which results in very similar characteristics
between the train and test sets. This is not a problem concerning thecomparison of the performances of various models. However, whenthe evaluation purpose is to compare the dataset performances, thisis not ideal because the performance of a model trained on data similar to test data will be naturally higher, hindering a fair comparison.
Thus, we decided to build an independent test dataset to fairly evaluate the performance of our corpus.
We built the dataset by first collecting the audio and the subtitles
from the videos listed in the “TEDx talks in Japanese” playlist4 . For
each video with manual (i.e., not auto-generated) subtitles, we segmented the audio at the subtitle boundaries. We randomly selected
10,000 segments from all the audio segments of all talks. An annotator manually checked and modified each of them. During this step,
the timestamps were modified if they were inaccurate. Moreover,
the transcripts were re-written to exactly match the spoken words,
including fillers, if necessary. The dataset contained 10,000 audio
segments spoken by 273 unique speakers of different genders, ages,
and backgrounds. The total audio length was 8.8 hours. We released
the list of video URLs and the annotator’s modification; hence, exactly the same data can be reconstructed5 .
4.1.2. Acoustic and Language Models
We compared three different AMs, namely CSJ, TV, and CSJ+TV.
The CSJ model was the same as that used in Section 2.2. The TV
model was trained on our new corpus. The CSJ+TV model wastrained on both CSJ and TV corpora by simply combining them. All
AMs were trained with the same procedures as Kaldi’s official CSJ
recipe. We did not conduct any parameter tuning specific to our corpus to compare only the data difference. For the TV data, we did nothave speaker labels for each audio segment. Therefore we treatedeach speech segment as an utterance spoken by a unique speaker, including when computing the cepstral mean and variance normalization (CMVN), training GMMs with speaker adaptive training (SAT),
and training i-vector extractors.
We used 3-gram LMs trained with modified Kneser–Ney discounting using SRILM [21] in all experiments following the default
CSJ recipe. The LM built in the CSJ recipe (CSJ-LM) was used
for all the AMs to decode the CSJ evaluation dataset. We tested theLMs trained using two different text corpora in addition to the CSJLM to decode the TEDxJP-10K dataset. The TV-LM was built using
the transcripts of the training set of LaboroTVSpeech. The vocabulary size of the TV-LM was approximately 170K. We also built LMs
using the OSCAR [22] dataset containing 100 GB of web-crawled
Japanese texts. We cleansed the data by removing special symbols
such as emojis, and tokenized the whole data in the same procedures
as described in Section 2. In building OSCAR-LM, we selected
200K vocabulary based on the frequency counts of words. Subsequently, 2 and 3-gram probabilities were pruned with a threshold of
4 https://www.youtube.com/playlist?list=PLsRNoUx
8w3rOHjXIU5EE4KOiIagv9yQaG
5 https://github.com/laboroai/TEDxJP-10K

Table 3: ASR decoding results (CER%) on the TEDxJP-10K
dataset.
Acoustic model
LM
CSJ
TV
CSJ+TV
CSJ
23.41 21.83
20.61
TV
22.14 18.98
18.22
OSCAR
23.52 19.28
18.71
TV+OSCAR
21.89 18.29
17.92
Table 4: ASR decoding results (CER%) on the CSJ eval dataset
using CSJ-LM.
Acoustic model
DatasetCSJ
TV
CSJ+TVeval1
8.00 14.46
7.83
eval2
6.44 11.30
6.62
eval3
5.94 10.94
6.22
10−8 . We also examined the interpolating TV-LM and OSCAR-LM(TV+OSCAR). Their interpolation weights were estimated with an
expectation–maximization (EM) algorithm using the transcripts ofthe development data.
4.2. Experimental Results
Table 3 presents the ASR performance in the CER for the TEDxJP10K dataset. Comparing CSJ and TV, TV consistently showed
better performances. When TV+OSCAR-LM was used, TV-AM
improved CER by 3.6% points from CSJ-AM, clearly showing that
the AM built using our corpus is more capable of recognizing speech
under realistic conditions. Moreover, combining the training dataof the CSJ and our TV corpus showed a noticeable decrease in
the CER. The improvement from CSJ-AM was 4.6% points when
TV+OSCAR-LM was used.
Table 4 shows the ASR performance in the CER for the CSJ evaldataset. The CSJ-AM showed a better performance than TV-AM because the acoustic domain of the CSJ eval dataset matched that of
the CSJ training data, as discussed in Section 4.1.1. However, by
combining the two corpora (CSJ+TV), the results significantly approached those of the CSJ-AM. In eval1, CSJ+TV provided a slight
improvement of 0.17% points.
Table 3 and Table 4 show that CSJ+TV-AM exhibited promising
results in both ASR tasks. These results demonstrate that our new
corpus is useful for training an ASR system by itself, or even in
combination with other corpora.
5. CONCLUSIONSThis study introduced LaboroTVSpeech, a new Japanese ASR corpus containing over 2,000 hours of various speeches. The corpus
was built using Japanese broadcast TV recordings and their subtitles, which are not always verbatim. The corpus was made available for academic usage. We described the statistical details of the
corpus and evaluated its practicality through two ASR tasks. The
experiment results showed that our new corpus is especially usefulfor building ASR systems that can recognize speech with various
acoustic characteristics.
One key advantage of our corpus is that its size increases as timegoes on because we use broadcast TV programs as data sources.
Therefore we plan to regulary update the corpus with a larger amountof data. Moreover, the genre information on TV recordings allows
us to make subsets of the whole dataset containing speech segmentsof a specific genre. Such sub-datasets would be useful particularly
in building an ASR system for a specific domain.

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