【成蹊大学】教員検索

世木 寛之

セギ ヒロユキ  (Hiroyuki SEGI)

基本情報

所属
成蹊大学 理工学部 理工学科 教授
学位
博士(工学)(慶應義塾大学)

J-GLOBAL ID
201501025877783683
researchmap会員ID
B000244685

研究キーワード

 2

論文

 25
  • Ai Mizota, Hiroyuki Segi
    2021 IEEE International Conference on Consumer Electronics (ICCE) 2021年1月10日  査読有り
  • Hiroyuki Segi, Shoei Sato, Kazuo Onoe, Akio Kobayashi, Akio Ando
    Artificial Intelligence: Concepts, Methodologies, Tools, and Applications 3 2021-2037 2016年12月12日  査読有り
    Tied-mixture HMMs have been proposed as the acoustic model for large-vocabulary continuous speech recognition and have yielded promising results. They share base-distribution and provide more flexibility in choosing the degree of tying than state-clustered HMMs. However, it is unclear which acoustic models to superior to the other under the same training data. Moreover, LBG algorithm and EM algorithm, which are the usual training methods for HMMs, have not been compared. Therefore in this paper, the recognition performance of the respective HMMs and the respective training methods are compared under the same condition. It was found that the number of parameters and the word error rate for both HMMs are equivalent when the number of codebooks is sufficiently large. It was also found that training method using the LBG algorithm achieves a 90% reduction in training time compared to training method using the EM algorithm, without degradation of recognition accuracy.
  • Segi Hiroyuki
    INTERNATIONAL JOURNAL OF MULTIMEDIA DATA ENGINEERING & MANAGEMENT 7(2) 53-67 2016年4月  査読有り
  • 世木寛之
    成蹊大学理工学研究報告 52(2) 5-10 2015年12月  
    The 'Kabushiki Shikyo' program broadcast on NHK Radio 2 reports on the daily closing prices and net changes of about 830 stocks listed on the Tokyo Stock Exchange. Reading out the numerical values without making mistakes within the allotted broadcast time can be extremely difficult for the announcers. We have therefore developed an automatic broadcast system for stock-price bulletins, which uses numerical speech synthesis and automatic speech-rate conversion. Our system has been used in experimental digital terrestrial radio broadcasts since October 2006 and also used in NHK radio 2 since March 2010. This article describes the generation of texts to build the speech waveform database, the mechanism used to synthesize numerical speech via the database, and the evaluation of naturalness for the synthesized speech samples.
  • Hiroyuki Segi, Kazuo Onoe, Shoei Sato, Akio Kobayashi, Akio Ando
    Journal of Information Technology Research 7(3) 15-31 2014年7月1日  査読有り
    Tied-mixture HMMs have been proposed as the acoustic model for large-vocabulary continuous speech recognition and have yielded promising results. They share base-distribution and provide more flexibility in choosing the degree of tying than state-clustered HMMs. However, it is unclear which acoustic models to superior to the other under the same training data. Moreover, LBG algorithm and EM algorithm, which are the usual training methods for HMMs, have not been compared. Therefore in this paper, the recognition performance of the respective HMMs and the respective training methods are compared under the same condition. It was found that the number of parameters and the word error rate for both HMMs are equivalent when the number of codebooks is sufficiently large. It was also found that training method using the LBG algorithm achieves a 90% reduction in training time compared to training method using the EM algorithm, without degradation of recognition accuracy.

MISC

 29
  • 世木 寛之, 清山 信正, 田高 礼子
    NHK技研R&D (131) 40-47 2012年1月  
  • 世木 寛之, 田高 礼子, 清山 信正, 都木 徹
    情報処理学会論文誌 50(2) 575-586 2009年2月15日  
    大規模な音声データベースから音声データを選択して接続する波形接続型音声合成が提案されている.この音声合成方式で利用される大規模音声データベースは,音韻バランスなどを考慮して選定された文章を,音声合成に適した話速やスタイルで読み上げることで作成されることが多い.一方,放送局では過去に放送された番組が大量に保存されているため,これらを音声データベースとして利用することが考えられる.本研究では,ニュース番組の収録音声を,波形接続型音声合成システムの音声データベースとして利用することを試みた.高い頻度で音声データベースに存在する音素列を,前後の音素環境を考慮して抽出した"音素環境依存音素列"を探索単位として合成音を作成し,5段階のオピニオン評価実験を行った結果,MOSは4.01となり,「不自然な部分はあるが気にならない」という自然性を持つ合成音が得られた.特に,全体の39.8%が5の「自然である」と評価され,自然音声と変わらない品質の合成音がかなりの頻度で作成されていることが分かった.次に,目標スコアを用いた場合と,用いない場合の合成音とを比較したところ,MOSの差は0.18となり,音声データベースの発話内容と合成する文が類似している場合には,必ずしも韻律予測せず目標スコアを考慮しなくても,自然性の高い合成音を作成できる可能性が示された.Proposals have been made to implement a system that generates synthesized speech by concatenating segments of speech stored in large databases. While these databases are often created by recording sentences with a specific phonetic balance, read at a rate and in a style that are optimal for speech synthesis, this paper explores an alternative method of database creation, one that utilizes broadcast materials archived in networks. In our study, we used samples of recorded speech from news programs to create a speech database. An assessment of speech generated by the speech synthesis method using "context dependent phoneme sequences" as search units yielded the mean opinion score (MOS) of 4.01 in a one-to-five-scale rating. Overall, the samples were considered "somewhat unnatural but not bothersome." In particular, 39.8% of the entire samples scored 5.0, demonstrating their highly natural-sounding quality. In addition, we compared the evaluation on "synthesized speech with target scores" and that on "synthesized speech without target scores." The difference of MOS was 0.18. This result confirmed that prosody prediction or target scores are not necessarily required to create synthesized speech of natural-sounding quality when the content of input sentences is similar to the content of sentences stored in the database.
  • 世木 寛之, 清山 信正, 田高 礼子
    放送技術 61(4) 91-96 2008年4月  
  • 田高 礼子, 世木 寛之, 清山 信正
    聴覚研究会資料 38(2) 159-164 2008年3月20日  

書籍等出版物

 1
  • 八木伸行監修, 世木寛之ほか著 (担当:分担執筆, 範囲:第11章音声合成)
    オーム社 2008年7月

講演・口頭発表等

 47

共同研究・競争的資金等の研究課題

 1

産業財産権

 72