Robust speech recognition via enhancing the complex-valued acoustic spectrum in modulation domain

Jeih Weih Hung, Hsin Ju Hsieh, Berlin Chen

研究成果: 雜誌貢獻期刊論文同行評審

14 引文 斯高帕斯(Scopus)


The purpose of this paper is to develop a novel speech feature extraction framework for independently compensating the real and imaginary acoustic spectra of speech signals in the modulation domain with the techniques of histogram equalization (HEQ) and non-negative matrix factorization (NMF). By doing so, we can enhance not only the magnitude but also the phase components of the acoustic spectra, thereby creating noise-robust speech features. More specifically, the proposed framework makes the following three major contributions: First, via either of the HEQ and NMF operations, the long-term cross-frame correlation among the acoustic spectra at the same frequency can be captured to compensate for the spectral distortion caused by noise. Second, the noise effect can be handled in a high acoustic frequency resolution. Finally, the distortion dwelt in the acoustic spectra can be more extensively mitigated due to the independent processes for the respective real and imaginary parts. The evaluation experiments were carried out on the Aurora-2 and Aurora-4 benchmark tasks, and the corresponding results suggest that our proposed methods can achieve performance competitive to or better than many widely used noise robustness methods, including the well-known advanced front-end (AFE) extraction scheme, in speech recognition.

頁(從 - 到)236-251
期刊IEEE/ACM Transactions on Audio Speech and Language Processing
出版狀態已發佈 - 2016 2月

ASJC Scopus subject areas

  • 電腦科學(雜項)
  • 聲學與超音波
  • 計算數學
  • 電氣與電子工程


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