カテゴリ: 論文誌(論文単位)グループ名: 【C】電子・情報・システム部門発行日: 2015/10/01タイトル(英語): Robust Speech Recognition against Misdetection Using Whole-Word HMMs and Relaxed Algorithm for Likelihood Calculation著者名: 早坂 昇（大阪電気通信大学情報通信工学部情報工学科）著者名(英語): Noboru Hayasaka (Dept. of Engineering Informatics, Osaka Electro-Communication University)キーワード: 孤立単語認識，区間検出誤り，単語モデル，制約緩和尤度計算 Isolated-word recognition，Misdetection，Whole-word HMM，Relaxed algorithm for likelihood calculation要約(英語): In this paper, we propose a new framework that employs whole-word hidden Markov models with a relaxed algorithm for likelihood calculation. Although voice activity detection (VAD) is an essential technique in order to provide a sophisticated voice interface, a perfect VAD has not been proposed yet. An incomplete VAD leads to misdetections frequently. Since the conventional likelihood calculation, which has limitation in edge states, assumes that VAD works well, we cannot obtain sufficient recognition accuracy against the misdetections. The proposed method is a technique to improve the performance against misdetected speech in the framework which uses whole-word HMMs. The method employs an algorithm which relaxes the limitation of likelihood calculation in the edge states. In order to verify the effectiveness of the proposed method, we carried out recognition experiments for artificially shortened segments. With the conventional method, the average recognition rates were 91.81%, 91.50%, 89.56%, and 85.13% under 3, 4, 5, and 6 states/phoneme conditions, respectively. However, the proposed method allowed us to achieve 91.88%, 92.64%, 92.74%, and 93.14% under the same conditions. Furthermore, we confirmed the effectiveness of the proposed method through experiments combined with CENSREC-1-C.本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.135 No.10 （2015） 特集：産業界を支える情報通信技術本誌掲載ページ: 1236-1243 p原稿種別: 論文／日本語電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/135/10/135_1236/_article/-char/ja/
