Time-Reversal Enhancement Network With Cross-Domain Information for Noise-Robust Speech Recognition

Fu An Chao, Jeih Weih Hung, Tommy Sheu, Berlin Chen

研究成果: 雜誌貢獻期刊論文同行評審

1 引文 斯高帕斯(Scopus)


Due to the enormous progress in deep learning, speech enhancement (SE) techniques have shown promising efficacy and play a pivotal role prior to an automatic speech recognition (ASR) system to mitigate the noise effects. In this article, we put forward a novel cross-domain time-reversal enhancement network (CD-TENET). CD-TENET leverages the time-reversed version of a speech signal and two effective features that consider the phase information of a speech signal in the time domain and the frequency domain, respectively, to promote SE performance for noise-robust ASR. Extensive experiments demonstrate that CD-TENET can not only recover the original speech effectively but also improve both SE and ASR performance simultaneously. More surprisingly, the proposed CD-TENET method can offer a marked relative word error rate reduction on test utterances of scenarios contaminated with unseen noises when compared to a strong baseline with the multicondition training setting.

頁(從 - 到)114-124
期刊IEEE Multimedia
出版狀態已發佈 - 2022

ASJC Scopus subject areas

  • 軟體
  • 訊號處理
  • 媒體技術
  • 硬體和架構
  • 電腦科學應用


深入研究「Time-Reversal Enhancement Network With Cross-Domain Information for Noise-Robust Speech Recognition」主題。共同形成了獨特的指紋。