Voice activity detection using frame-wise model re-estimation method based on Gaussian pruning with weight normalization

Masakiyo Fujimoto*, Shinji Watanabe, Tomohiro Nakatani

*この研究の対応する著者

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

This paper proposes a frame-wise model re-estimation method based on Gaussian pruning with weight normalization for noise robust voice activity detection (VAD). Our previous work, switching Kalman filter-based VAD, sequentially estimates a non-stationary noise Gaussian mixture model (GMM) and constructs GMMs of observed noisy speech signals by composing pre-trained silence and clean GMMs and sequentially estimated noise GMMs. However, the composed models are not optimal, because they do not fully reflect the characteristics of the observed signal. Thus, to ensure the optimality of the composed models, we investigate a method for re-estimating the composed model. Since our VAD method works under the frame-wise sequential processing, there are insufficient re-training data for re-estimation of whole model parameters. To solve this problem, we propose a model re-estimation method that involves the extraction of reliable information using Gaussian pruning with weight normalization. Namely, the proposed method re-estimates the model by pruning non-dominant Gaussian distributions in expressing the local characteristics of each frame and by normalizing Gaussian weights of remaining distributions.

本文言語English
ホスト出版物のタイトルProceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010
出版社International Speech Communication Association
ページ3102-3105
ページ数4
出版ステータスPublished - 2010
外部発表はい

出版物シリーズ

名前Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010

ASJC Scopus subject areas

  • 言語および言語学
  • 言語聴覚療法

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