研究者詳細 - 原　直

論文 - 原　直

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New approach to emotional information exchange: Experience metaphor based on life logs 査読国際誌

Masanobu Abe, Daisuke Fujioka, Kazuto Hamano, Sunao Hara, Rika Mochizuki, Tomoki Watanabe

2014 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom 2014 Workshops 191 - 194 2014年3月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：IEEE

We are striving to develop a new communication technology based on individual experiences that can be extracted from life logs. We have proposed the "Emotion Communication Model" and confirmed that significant correlation exists between experience and emotion. As the second step, particularly addressing impressive places and events, this paper describes an investigation of the extent to which we can share emotional information with others through individuals' experiences. Subjective experiments were conducted using life log data collected during 7-47 months. Experiment results show that (1) impressive places are determined by the distance from home, visit frequency, and direction from home and that (2) positive emotional information is highly consistent among people (71.4%), but it is not true for negative emotional information. Therefore, experiences are useful as metaphors to express positive emotional information.

DOI： 10.1109/PerComW.2014.6815198

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Development of a Toolkit Handling Multiple Speech-Oriented Guidance Agents for Mobile Applications 査読国際誌

Sunao Hara, Hiromichi Kawanami, Hiroshi Saruwatari, Kiyohiro Shikano

Natural Interaction with Robots, Knowbots and Smartphones, Putting Spoken Dialog Systems into Practice 79 - 85 2014年

Evaluation of Invalid Input Discrimination Using Bag-of-Words for Speech-Oriented Guidance System 査読国際誌

Haruka Majima, Rafael Torres, Hiromichi Kawanami, Sunao Hara, Tomoko Matsui, Hiroshi Saruwatari, Kiyohiro Shikano

Natural Interaction with Robots, Knowbots and Smartphones, Putting Spoken Dialog Systems into Practice 389 - 397 2014年

A Hybrid Text-to-Speech Based on Sub-Band Approach 査読国際誌

Takuma Inoue, Sunao Hara, Masanobu Abe

Proceedings of Asia-Pacific Signal and Information Processing Association 2014 Annual Summit and Conference 1 - 4 2014年

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：IEEE

This paper proposes a sub-band speech synthesis approach to develop high-quality Text-to-Speech (TTS). For the low-frequency band and high-frequency band, Hidden Markov Model (HMM)-based speech synthesis and waveform -based speech synthesis are used, respectively. Both speech synthesis methods are widely known to show good performance and to have benefits and shortcomings from different points of view. One motivation is to apply the right speech synthesis method in the right frequency band. Experiment results show that in terms of the smoothness the proposed approach shows better performance than waveform -based speech synthesis, and in terms of the clarity it shows better than HMM-based speech synthesis. Consequently, the proposed approach combines the inherent benefits from both waveform-based speech synthesis and HMM-based speech synthesis.

DOI： 10.1109/APSIPA.2014.7041575

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音声情報案内システムにおけるBag-of-Wordsを用いた無効入力の棄却査読

真嶋温佳, 藤田洋子, トーレス・ラファエル, 川波弘道, 原直, 松井知子, 猿渡洋, 鹿野清宏

情報処理学会論文誌 54 ( 2 ) 443 - 451 2013年2月

記述言語：日本語出版者・発行元：情報処理学会

実環境における音声認識を用いた情報案内システムでは，雑音等の非音声やユーザ同士の背景会話など，システムへの入力として不適切な入力が存在する．これらの入力はシステムの誤作動・誤認識の原因となるので，システムへの入力として適切な入力（有効入力）と不適切な入力（無効入力）の識別を行い，無効入力を棄却することにより，無効入力に対する応答処理を行わないことが重要である．従来，有効入力と無効入力との識別には，メル周波数ケプストラム係数などの音響的特徴量によるGMM（Gaussian Mixture Model）が用いられる．しかし，入力データの音声認識結果から得られる言語的な情報を使うことにより，システムのタスクを考えたうえで有効入力と無効入力の識別が可能になると考えられる．そこで本論文では，音響特徴量にBag-of-Words（BOW）を言語的特徴量として併用した無効入力の識別を検討した．識別手法としては，サポートベクタマシン（SVM）および最大エントロピー法を用いた．実験には実環境音声情報案内システム「たけまるくん」の入力データを用いた．SVMによる識別結果では，GMMによる音響尤度のみを用いた場合に比べて，BOWを用いた場合，F尺度を82.19%から85.41%に改善することができた．さらに，GMMによる音響尤度，発話時間，SNRを組み合わせた特徴量にBOWを追加することで，F尺度を86.58%まで改善することができた．詳細な分析の結果，BOWは特に無効入力の誤受理を減らす効果があることが示された．On a real environment speech-oriented information guidance system, a valid and invalid input discrimination is important as invalid inputs such as noise, laugh, cough and utterances between users lead to unpredictable system responses. Generally, acoustic features such as MFCC (Mel-Frequency Cepstral Coefficient) are used for discrimination. Comparing acoustic likelihoods of GMMs (Gaussian Mixture Models) from speech data and noise data is one of the typical methods. In addition to that, using linguistic features, such as speech recognition result, is considered to improve discrimination accuracy as it reflects the task-domain of invalid inputs and meaningless recognition results from noise inputs. In this paper, we introduce Bag-of-Words (BOW) as a feature to discriminate between valid and invalid inputs. Support Vector Machine (SVM) and Maximum Entropy method (ME) are also employed to realize robust classification. We experimented the methods using real environment data obtained from the guidance system "Takemaru-kun." By applying BOW on SVM, the F-measure is improved to 85.09%, from 82.19% when using GMMs. In addition, experiments using features combining BOW with acoustic likelihoods from GMMs, Duration and SNR were conducted, improving the F-measure to 86.58%.

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音声対話システムの発話・動作タグN-gramを用いた課題未達成のオンライン検出査読

原直, 北岡教英, 武田一哉

電子情報通信学会論文誌 (D) J96-D ( 1 ) 81 - 93 2013年1月

担当区分：筆頭著者記述言語：日本語出版者・発行元：一般社団法人電子情報通信学会

本論文ではN-gram特徴を用いた音声対話システム利用時の課題未達成対話の検出手法を提案する.実験にはユーザが自分のPC上で利用するという,実環境下で収録された楽曲検索のための音声対話システムとの対話データを利用する.楽曲検索課題を行っている全ての対話データはユーザとシステムの発話を抽象化した発話・動作ダグにより記述し,そのタグ系列をN-gramとしてモデル化を行う.本研究では,タグ系列中のタグN-gramの出現回数を素性として,Support Vector MachineやC4.5決定木などの識別的手法による課題未達成対話の検出実験を行った.更に対話長や反応時間などの対話の特徴を表す対話変数を導入することで検出性能の向上を行った.その結果,ユーザの4回目の発話までを用いることで,約76%の識別率となった.

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その他リンク： http://search.ieice.org/bin/summary.php?id=j96-d_1_81&category=D&year=2013&lang=J&abst=

Acoustic Model Training Using Pseudo-Speaker Features Generated by MLLR Transformations for Robust Speaker-Independent Speech Recognition 査読

Arata Itoh, Sunao Hara, Norihide Kitaoka, Kazuya Takeda

IEICE TRANSACTIONS on Information and Systems E95D ( 10 ) 2479 - 2485 2012年10月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG

A novel speech feature generation-based acoustic model training method for robust speaker-independent speech recognition is proposed. For decades, speaker adaptation methods have been widely used. All of these adaptation methods need adaptation data. However, our proposed method aims to create speaker-independent acoustic models that cover not only known but also unknown speakers. We achieve this by adopting inverse maximum likelihood linear regression (MLLR) transformation-based feature generation, and then we train our models using these features. First we obtain MLLR transformation matrices from a limited number of existing speakers. Then we extract the bases of the MLLR transformation matrices using PCA. The distribution of the weight parameters to express the transformation matrices for the existing speakers are estimated. Next, we construct pseudo-speaker transformations by sampling the weight parameters from the distribution, and apply the transformation to the normalized features of the existing speaker to generate the features of the pseudo-speakers. Finally, using these features, we train the acoustic models. Evaluation results show that the acoustic models trained using our proposed method are robust for unknown speakers.

DOI： 10.1587/transinf.E95.D.2479

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その他リンク： http://search.ieice.org/bin/summary.php?id=e95-d_10_2479

Causal analysis of task completion errors in spoken music retrieval interactions 査読国際誌

Sunao Hara, Norihide Kitaoka, Kazuya Takeda

Proceedings of the 8th international conference on Language Resources and Evaluation (LREC 2012) 1365 - 1372 2012年5月

担当区分：筆頭著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA

In this paper, we analyze the causes of task completion errors in spoken dialog systems, using a decision tree with N-gram features of the dialog to detect task-incomplete dialogs. The dialog for a music retrieval task is described by a sequence of tags related to user and system utterances and behaviors. The dialogs are manually classified into two classes: completed and uncompleted music retrieval tasks. Differences in tag classification performance between the two classes are discussed. We then construct decision trees which can detect if a dialog finished with the task completed or not, using information gain criterion. Decision trees using N-grams of manual tags and automatic tags achieved 74.2% and 80.4% classification accuracy, respectively, while the tree using interaction parameters achieved an accuracy rate of 65.7%. We also discuss more details of the causality of task incompletion for spoken dialog systems using such trees.

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その他リンク： http://www.lrec-conf.org/proceedings/lrec2012/summaries/1059.html

Robust seed model training for speaker adaptation using pseudo-speaker features generated by inverse CMLLR transformation 査読国際誌

Arata Itoh, Sunao Hara, Norihide Kitaoka, Kazuya Takeda

Proceedings of 2011 Automatic Speech Recognition and Understanding Workshop (ASRU 2011) 169 - 172 2011年12月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：IEEE

In this paper, we propose a novel acoustic model training method which is suitable for speaker adaptation in speech recognition. Our method is based on feature generation from a small amount of speakers' data. For decades, speaker adaptation methods have been widely used. Such adaptation methods need some amount of adaptation data and if the data is not sufficient, speech recognition performance degrade significantly. If the seed models to be adapted to a specific speaker can widely cover more speakers, speaker adaptation can perform robustly. To make such robust seed models, we adopt inverse maximum likelihood linear regression (MLLR) transformation-based feature generation, and then train our seed models using these features. First we obtain MLLR transformation matrices from a limited number of existing speakers. Then we extract the bases of the MLLR transformation matrices using PCA. The distribution of the weight parameters to express the MLLR transformation matrices for the existing speakers is estimated. Next we generate pseudo-speaker MLLR transformations by sampling the weight parameters from the distribution, and apply the inverse of the transformation to the normalized existing speaker features to generate the pseudo-speakers' features. Finally, using these features, we train the acoustic seed models. Using this seed models, we obtained better speaker adaptation results than using simply environmentally adapted models. © 2011 IEEE.

DOI： 10.1109/ASRU.2011.6163925

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Training Robust Acoustic Models Using Features of Pseudo-Speakers Generated by Inverse CMLLR Transformations 査読国際誌

Arata Itoh, Sunao Hara, Norihide Kitaoka, Kazuya Takeda

Proceedings of 2011 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC 2011) 1 - 5 2011年12月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：APSIPA

In this paper a novel speech feature generationbased acoustic model training method is proposed. For decades, speaker adaptation methods have been widely used. All existing adaptation methods need adaptation data. However, our proposed method creates speaker-independent acoustic models that cover not only known but also unknown speakers. We do this by adopting inverse maximum likelihood linear regression (MLLR) transformation-based feature generation, and then train our models using these features. First we obtain MLLR transformation matrices from a limited number of existing speakers. Then we extract the bases of the MLLR transformation matrices using PCA. The distribution of the weight parameters to express the MLLR transformation matrices for the existing speakers are estimated. Next we generate pseudo-speaker MLLR transformations by sampling the weight parameters from the distribution, and apply the inverse of the transformation to the normalized existing speaker features to generate the pseudospeakers' features. Finally, using these features, we train the acoustic models. Evaluation results show that the acoustic models which are created are robust for unknown speakers.

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On-line detection of task incompletion for spoken dialog systems using utterance and behavior tag N-gram vectors 査読国際誌

Sunao Hara, Norihide Kitaoka, Kazuya Takeda

Proceedings of the Paralinguistic Information and Its Integration in Spoken Dialogue Systems Workshop 215 - 225 2011年9月

Detection of task-incomplete dialogs based on utterance-and-behavior tag N-gram for spoken dialog systems 査読国際誌

Sunao Hara, Norihide Kitaoka, Kazuya Takeda

Proceedings of INTERSPEECH 2011 1312 - 1315 2011年

担当区分：筆頭著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：ISCA-INT SPEECH COMMUNICATION ASSOC

We propose a method of detecting "task incomplete" dialogs in spoken dialog systems using N-gram-based dialog models. We used a database created during a field test in which inexperienced users used a client-server music retrieval system with a spoken dialog interface on their own PCs. In this study, the dialog for a music retrieval task consisted of a sequence of user and system tags that related their utterances and behaviors. The dialogs were manually classified into two classes: the dialog either completed the music retrieval task or it didn't. We then detected dialogs that did not complete the task, using N-gram probability models or a Support Vector Machine with N-gram feature vectors trained using manually classified dialogs. Off-line and on-line detection experiments were conducted on a large amount of real data, and the results show that our proposed method achieved good classification performance.

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その他リンク： http://www.isca-speech.org/archive/interspeech_2011/i11_1305.html

Music Recommendation System Based on Human-to-human Conversation Recognition 査読国際誌

Hiromasa Ohashi, Sunao Hara, Norihide Kitaoka, Kazuya Takeda

Workshop Proceedings of the 7th International Conference on Intelligent Environments: Ambient Intelligence and Smart Environments 10 352 - 361 2011年

記述言語：英語掲載種別：論文集(書籍)内論文出版者・発行元：IOS PRESS

We developed an ambient system that plays music suitable for the mood of a human-human conversation using words obtained from a continuous-speech recognition system. Using the correspondence between a document space based on the texts related to the music and an acoustic space that expresses various audio features, the continuous-speech recognition results are mapped to an acoustic space. We performed a subjective evaluation of the system. The subjects rated the recommended music and the result reveals that the 10 most highly recommended selections included suitable music.

DOI： 10.3233/978-1-60750-795-6-352

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Automatic detection of task-incompleted dialog for spoken dialog system based on dialog act N-gram 査読国際誌

Sunao Hara, Norihide Kitaoka, Kazuya Takeda

Proceedings of INTERSPEECH2010 3034 - 3037 2010年9月

担当区分：筆頭著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：ISCA

In this paper, we propose a method of detecting task-incompleted users for a spoken dialog system using an N-gram-based dialog history model. We collected a large amount of spoken dialog data accompanied by usability evaluation scores by users in real environments. The database was made by a field test in which naive users used a client-server music retrieval system with a spoken dialog interface on their own PCs. An N-gram model was trained from sequences that consist of user dialog acts and/or system dialog acts for two dialog classes, that is, the dialog completed the music retrieval task or the dialog incompleted the task. Then the system detects unknown dialogs that is not completed the task based on the N-gram likelihood. Experiments were conducted on large real data, and the results show that our proposed method achieved good classification performance. When the classifier correctly detected all of the task-incompleted dialogs, our proposed method achieved a false detection rate of 6%.

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その他リンク： http://www.isca-speech.org/archive/interspeech_2010/i10_3034.html

Rapid acoustic model adaptation using inverse MLLR-based feature generation 査読国際誌

Arata Ito, Sunao Hara, Norihide Kitaoka, Kazuya Takeda

Proceedings of ICA2010 5 1 - 6 2010年8月

Estimation method of user satisfaction using N-gram-based dialog history model for spoken dialog system 査読国際誌

Sunao Hara, Norihide Kitaoka, Kazuya Takeda

LREC 2010 - SEVENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION 78 - 83 2010年5月

担当区分：筆頭著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA

In this paper, we propose an estimation method of user satisfaction for a spoken dialog system using an N-gram-based dialog history model. We have collected a large amount of spoken dialog data accompanied by usability evaluation scores by users in real environments. The database is made by a field-test in which naive users used a client-server music retrieval system with a spoken dialog interface on their own PCs. An N-gram model is trained from the sequences that consist of users' dialog acts and/or the system's dialog acts for each one of six user satisfaction levels: from 1 to 5 and phi (task not completed). Then, the satisfaction level is estimated based on the N-gram likelihood. Experiments were conducted on the large real data and the results show that our proposed method achieved good classification performance; the classification accuracy was 94.7% in the experiment on a classification into dialogs with task completion and those without task completion. Even if the classifier detected all of the task incomplete dialog correctly, our proposed method achieved the false detection rate of only 6%.

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その他リンク： http://www.lrec-conf.org/proceedings/lrec2010/summaries/579.html

Data collection and usability study of a PC-based speech application in various user environments 査読国際誌

Sunao Hara, Chiyomi Miyajima, Katsunobu Ito, Norihide Kitaoka, Kazuya Takeda

Proceedings of Oriental-COCOSDA 2008 39 - 44 2008年11月

In-car Speech Data Collection along with Various Multimodal Signals 査読国際誌

Akira Ozaki, Sunao Hara, Takashi Kusakawa, Chiyomi Miyajima, Takanori Nishino, Norihide Kitaoka, Katunobu Itou, Kazuya Takeda

Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08) 1846 - 1851 2008年5月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA

In this paper, a large-scale real-world speech database is introduced along with other multimedia driving data. We designed a data collection vehicle equipped with various sensors to synchronously record twelve-channel speech, three-channel video, driving behavior including gas and brake pedal pressures, steering angles, and vehicle velocities, physiological signals including driver heart rate, skin conductance, and emotion-based sweating on the palms and soles, etc. These multimodal data are collected while driving on city streets and expressways under four different driving task conditions including two kinds of monologues, human-human dialog, and human-machine dialog. We investigated the response timing of drivers against navigator utterances and found that most overlapped with the preceding utterance due to the task characteristics and the features of Japanese. When comparing utterance length, speaking rate, and the filler rate of driver utterances in human-human and human-machine dialogs, we found that drivers tended to use longer and faster utterances with more fillers to talk with humans than machines.

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その他リンク： http://www.lrec-conf.org/proceedings/lrec2008/summaries/472.html

多様な音響環境下における音声認識システム利用時のデータ収集システム査読

原直, 宮島千代美, 伊藤克亘, 武田一哉

電子情報通信学会論文誌(D) J90-D ( 10 ) 2807 - 2816 2007年10月

担当区分：筆頭著者記述言語：日本語掲載種別：研究論文（学術雑誌）出版者・発行元：一般社団法人電子情報通信学会

多様なPC利用環境において音声認識の被験者実験を行うために,WWWを利用した音声対話システム及びそのデータ収集システムを構築した.本システムのユーザはPC上にインストールされたクライアントプログラムを用いることで音声対話によりPC内に蓄積された音楽を再生したり,音楽ダウンロードサイトの楽曲を試聴することができる.クライアントプログラムの利用により収集された音声データや対話ログはインターネットを介してサーバ上に蓄積される.サーバには,ユーザごとの辞書のカスタマイズ等の機能も実装している.本システムをインターネット上で試験的に公開し,2か月間のフィールドテストとしてデータ収集を行ったところ合計59時間のシステム稼動時データを収集した.そのうち約5時間41分のデータ(11351個)が音声区間として検出されており,システムに対して発話されていた音声データとして6335発話を得た.

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An online customizable music retrieval system with a spoken dialogue interface 査読国際誌

Sunao Hara, Chiyomi Miyajima, Katsunobu Itou, Kazuya Takeda

The Journal of the Acoustical Society of America 120 ( 5-2 ) 3378 - 3379 2006年11月

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