Hiromitsu Nishizaki


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Handwritten Character Generation using Y-Autoencoder for Character Recognition Model Training
Tomoki Kitagawa | Chee Siang Leow | Hiromitsu Nishizaki
Proceedings of the Thirteenth Language Resources and Evaluation Conference

It is well-known that the deep learning-based optical character recognition (OCR) system needs a large amount of data to train a high-performance character recognizer. However, it is costly to collect a large amount of realistic handwritten characters. This paper introduces a Y-Autoencoder (Y-AE)-based handwritten character generator to generate multiple Japanese Hiragana characters with a single image to increase the amount of data for training a handwritten character recognizer. The adaptive instance normalization (AdaIN) layer allows the generator to be trained and generate handwritten character images without paired-character image labels. The experiment shows that the Y-AE could generate Japanese character images then used to train the handwritten character recognizer, producing an F1-score improved from 0.8664 to 0.9281. We further analyzed the usefulness of the Y-AE-based generator with shape images, out-of-character (OOC) images, which have different character images styles in model training. The result showed that the generator could generate a handwritten image with a similar style to that of the input character.


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Semi-Automatic Construction and Refinement of an Annotated Corpus for a Deep Learning Framework for Emotion Classification
Jiajun Xu | Kyosuke Masuda | Hiromitsu Nishizaki | Fumiyo Fukumoto | Yoshimi Suzuki
Proceedings of the Twelfth Language Resources and Evaluation Conference

In the case of using a deep learning (machine learning) framework for emotion classification, one significant difficulty faced is the requirement of building a large, emotion corpus in which each sentence is assigned emotion labels. As a result, there is a high cost in terms of time and money associated with the construction of such a corpus. Therefore, this paper proposes a method of creating a semi-automatically constructed emotion corpus. For the purpose of this study sentences were mined from Twitter using some emotional seed words that were selected from a dictionary in which the emotion words were well-defined. Tweets were retrieved by one emotional seed word, and the retrieved sentences were assigned emotion labels based on the emotion category of the seed word. It was evident from the findings that the deep learning-based emotion classification model could not achieve high levels of accuracy in emotion classification because the semi-automatically constructed corpus had many errors when assigning emotion labels. In this paper, therefore, an approach for improving the quality of the emotion labels by automatically correcting the errors of emotion labels is proposed and tested. The experimental results showed that the proposed method worked well, and the classification accuracy rate was improved to 55.1% from 44.9% on the Twitter emotion classification task.

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Integrating Disfluency-based and Prosodic Features with Acoustics in Automatic Fluency Evaluation of Spontaneous Speech
Huaijin Deng | Youchao Lin | Takehito Utsuro | Akio Kobayashi | Hiromitsu Nishizaki | Junichi Hoshino
Proceedings of the Twelfth Language Resources and Evaluation Conference

This paper describes an automatic fluency evaluation of spontaneous speech. In the task of automatic fluency evaluation, we integrate diverse features of acoustics, prosody, and disfluency-based ones. Then, we attempt to reveal the contribution of each of those diverse features to the task of automatic fluency evaluation. Although a variety of different disfluencies are observed regularly in spontaneous speech, we focus on two types of phenomena, i.e., filled pauses and word fragments. The experimental results demonstrate that the disfluency-based features derived from word fragments and filled pauses are effective relative to evaluating fluent/disfluent speech, especially when combined with prosodic features, e.g., such as speech rate and pauses/silence. Next, we employed an LSTM based framework in order to integrate the disfluency-based and prosodic features with time sequential acoustic features. The experimental evaluation results of those integrated diverse features indicate that time sequential acoustic features contribute to improving the model with disfluency-based and prosodic features when detecting fluent speech, but not when detecting disfluent speech. Furthermore, when detecting disfluent speech, the model without time sequential acoustic features performs best even without word fragments features, but only with filled pauses and prosodic features.

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Improving Speech Recognition for the Elderly: A New Corpus of Elderly Japanese Speech and Investigation of Acoustic Modeling for Speech Recognition
Meiko Fukuda | Hiromitsu Nishizaki | Yurie Iribe | Ryota Nishimura | Norihide Kitaoka
Proceedings of the Twelfth Language Resources and Evaluation Conference

In an aging society like Japan, a highly accurate speech recognition system is needed for use in electronic devices for the elderly, but this level of accuracy cannot be obtained using conventional speech recognition systems due to the unique features of the speech of elderly people. S-JNAS, a corpus of elderly Japanese speech, is widely used for acoustic modeling in Japan, but the average age of its speakers is 67.6 years old. Since average life expectancy in Japan is now 84.2 years, we are constructing a new speech corpus, which currently consists of the utterances of 221 speakers with an average age of 79.2, collected from four regions of Japan. In addition, we expand on our previous study (Fukuda, 2019) by further investigating the construction of acoustic models suitable for elderly speech. We create new acoustic models and train them using a combination of existing Japanese speech corpora (JNAS, S-JNAS, CSJ), with and without our ‘super-elderly’ speech data, and conduct speech recognition experiments. Our new acoustic models achieve word error rates (WER) as low as 13.38%, exceeding the results of our previous study in which we used the CSJ acoustic model adapted for elderly speech (17.4% WER).


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Designing an Evaluation Framework for Spoken Term Detection and Spoken Document Retrieval at the NTCIR-9 SpokenDoc Task
Tomoyosi Akiba | Hiromitsu Nishizaki | Kiyoaki Aikawa | Tatsuya Kawahara | Tomoko Matsui
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We describe the evaluation framework for spoken document retrieval for the IR for the Spoken Documents Task, conducted in the ninth NTCIR Workshop. The two parts of this task were a spoken term detection (STD) subtask and an ad hoc spoken document retrieval subtask (SDR). Both subtasks target search terms, passages and documents included in academic and simulated lectures of the Corpus of Spontaneous Japanese. Seven teams participated in the STD subtask and five in the SDR subtask. The results obtained through the evaluation in the workshop are discussed.


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Test Collections for Spoken Document Retrieval from Lecture Audio Data
Tomoyosi Akiba | Kiyoaki Aikawa | Yoshiaki Itoh | Tatsuya Kawahara | Hiroaki Nanjo | Hiromitsu Nishizaki | Norihito Yasuda | Yoichi Yamashita | Katunobu Itou
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

The Spoken Document Processing Working Group, which is part of the special interest group of spoken language processing of the Information Processing Society of Japan, is developing a test collection for evaluation of spoken document retrieval systems. A prototype of the test collection consists of a set of textual queries, relevant segment lists, and transcriptions by an automatic speech recognition system, allowing retrieval from the Corpus of Spontaneous Japanese (CSJ). From about 100 initial queries, application of the criteria that a query should have more than five relevant segments that consist of about one minute speech segments yielded 39 queries. Targeting the test collection, an ad hoc retrieval experiment was also conducted to assess the baseline retrieval performance by applying a standard method for spoken document retrieval.

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Developing Corpus of Japanese Classroom Lecture Speech Contents
Masatoshi Tsuchiya | Satoru Kogure | Hiromitsu Nishizaki | Kengo Ohta | Seiichi Nakagawa
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

This paper explains our developing Corpus of Japanese classroom Lecture speech Contents (henceforth, denoted as CJLC). Increasing e-Learning contents demand a sophisticated interactive browsing system for themselves, however, existing tools do not satisfy such a requirement. Many researches including large vocabulary continuous speech recognition and extraction of important sentences against lecture contents are necessary in order to realize the above system. CJLC is designed as their fundamental basis, and consists of speech, transcriptions, and slides that were collected in real university classroom lectures. This paper also explains the difference about disfluency acts between classroom lectures and academic presentations.


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An Empirical Study on Multiple LVCSR Model Combination by Machine Learning
Takehito Utsuro | Yasuhiro Kodama | Tomohiro Watanabe | Hiromitsu Nishizaki | Seiichi Nakagawa
Proceedings of HLT-NAACL 2004: Short Papers