Multichannel Speech Enhancement Based on Generalized Gamma Prior Distribution with Its Online Adaptive Estimation

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概要

• 論文の詳細を見る

• We present a multichannel speech enhancement method based on MAP speech spectral magnitude estimation using a generalized gamma model of speech prior distribution, where the model parameters are adapted from actual noisy speech in a frame-by-frame manner. The utilization of a more general prior distribution with its online adaptive estimation is shown to be effective for speech spectral estimation in noisy environments. Furthermore, the multi-channel information in terms of cross-channel statistics are shown to be useful to better adapt the prior distribution parameters to the actual observation, resulting in better performance of speech enhancement algorithm. We tested the proposed algorithm in an in-car speech database and obtained significant improvements of the speech recognition performance, particularly under non-stationary noise conditions such as music, air-conditioner and open window.

• （社）電子情報通信学会の論文
• 2008-03-01

著者

• TAKEDA Kazuya

  Nagoya University

• Takeda Kazuya

  Nagoya Univ.

• Takeda Kazuya

  Graduate School Of Information Science Nagoya University

• Takeda Kazuya

  Nagoya Univ. Nagoya‐shi Jpn

• Huy Dat

  Institute For Infocomm Research

• ITAKURA Fumitada

  Graduate School of Information Engineering, Meijo University

• HUY DAT

  Graduate School of Information Science, Nagoya University

• Itakura Fumitada

  The Faculty Of Science And Technology Meijo University

• Itakura Fumitada

  Graduate School Of Information Engineering Meijo University

• Takeda Kazuya

  Graduate School Of Information Science At Nagoya University

• ITAKURA Fumitada

  Institute for Infocomm Research

• Takeda Kazuya

  Graduate School of Engineering, Nagoya University:Center for Integrated Acoustic Information Research, Nagoya University

• TAKEDA Kazuya

  Graduate School of Engineering, Nagoya University

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