Enhancing feature modulation spectra with dictionary learning approaches for robust speech recognition

Bi Cheng Yan, Chin Hong Shih, Shih Hung Liu, Berlin Chen

研究成果: 書貢獻/報告類型會議論文篇章

摘要

Noise robustness has long garnered much interest from researchers and practitioners of the automatic speech recognition (ASR) community due to its paramount importance to the success of ASR systems. This paper presents a novel approach to improving the noise robustness of speech features, building on top of the dictionary learning paradigm. To this end, we employ the K-SVD method and its variants to create sparse representations with respect to a common set of basis spectral vectors that captures the intrinsic temporal structure inherent in the modulation spectra of clean training speech features. The enhanced modulation spectra of speech features, constructed by mapping the original modulation spectra into the space spanned by these representative basis vectors, can better carry noise-resistant acoustic characteristics. In addition, considering the nonnegative property of the modulation spectrum amplitudes, we utilize the nonnegative K-SVD method, in combination with the nonnegative sparse coding method, to generate more noise-robust speech features. All experiments were conducted and verified using the standard Aurora-2 database and task. The empirical results show that the proposed dictionary learning based approach can provide significant average word error reductions when being integrated with either a GMM-HMM or a DNN-HMM based ASR system.

原文英語
主出版物標題2017 IEEE International Conference on Multimedia and Expo, ICME 2017
發行者IEEE Computer Society
頁面577-582
頁數6
ISBN(電子)9781509060672
DOIs
出版狀態已發佈 - 2017 八月 28
事件2017 IEEE International Conference on Multimedia and Expo, ICME 2017 - Hong Kong, 香港
持續時間: 2017 七月 102017 七月 14

出版系列

名字Proceedings - IEEE International Conference on Multimedia and Expo
ISSN(列印)1945-7871
ISSN(電子)1945-788X

其他

其他2017 IEEE International Conference on Multimedia and Expo, ICME 2017
國家/地區香港
城市Hong Kong
期間2017/07/102017/07/14

ASJC Scopus subject areas

  • 電腦網路與通信
  • 電腦科學應用

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