Neural-network-based HMM adaptation for noisy speech recognition

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概要

• 論文の詳細を見る

• This paper proposes a new method, using neural networks, of adapting phone HMMs to noisy speech. The neural networks are designed to map clean speech HMMs to noise-adapted HMMs, using noise HMMs and signal-to-noise ratios (SNRs) as inputs. The neural network is trained by minimizing the mean square error between the output HMMs and the target noise-adapted HMMs, In an evaluation, the proposed method was used to recognize noisy broadcast-news speech in speaker-dependent and speaker-independent modes. The trained networks were found to be effective in recognizing new speakers under new noise and various SNR conditions.

• 社団法人日本音響学会の論文

著者

• ITOH Daisuke

  Department of Bio-organic Science, Obihiro University of Agriculture and Veterinary Medicine

• Furui S

  Tokyo Inst. Technol. Tokyo Jpn

• Furui Sadaoki

  Department Of Computer Science Graduate School Of Information Science And Engineering Tokyo Institut

• Itoh Daisuke

  Department Of Computer Science Tokyo Institute Of Technology

• Zhang Zhipeng

  Department Of Computer Science Tokyo Institute Of Technology

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