帯域別セグメンタルSNRとサポートベクトル回帰を用いた騒音下音声了解度推定

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: 2013/08/01

タイトル(英語): Speech Intelligibility Estimation using Support Vector Regression and Critical Band Segmental SNR in Noisy Condition

著者名: 小林　洋介（山形大学大学院理工学研究科），近藤　和弘（山形大学大学院理工学研究科）

著者名(英語): Yosuke Kobayashi (Yamagata University), Kazuhiro Kondo (Yamagata University)

キーワード: 音声了解度試験，サポートベクトル回帰，騒音クラスタリング，音声了解度推定　　speech intelligibility，support vector regression，ambient noise clustering，speech intelligibility estimation

要約(英語): In this paper, we propose a speech intelligibility estimation method using the Support Vector Regression (SVR) with the normalized segmental Signal-to-Noise Ratio in 25 critical bands (cbSNRseg). In the proposed method, estimation was done in the target 32 noise environments which were classified into 3 clusters by the ambient noise clustering method with MIR (Music Information Retrieval) features and the x-means algorithm. Next, We compared cbSNRseg and 1/3 octave bands SNRseg (obSNRseg) and used the cross-validation RMSE in 5 regression methods including SVR. As a result, the weighted sum of RMSE using cbSNRseg was better than obSNRseg with RMSE reduction factor of about 0.8 compared to all other regression methods. Finally, we compared the performance of each regression methods in open tests. As a result, the best regression method was the SVR using the RBF kernel, in which RMSE is reduced by a factor of about 0.7 compared to other regression methods.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.133 No.8 （2013） 特集：パワー半導体光源とその応用技術

本誌掲載ページ: 1556-1564 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/133/8/133_1556/_article/-char/ja/