標題: Robust SBR method for adverse Mandarin speech recognition
作者: Hong, WT
Chen, SH
電信工程研究所
Institute of Communications Engineering
公開日期: 27-五月-1999
摘要: An RNN-based robust signal bias removal (RRSBR) method is proposed for improving both the recognition performance and the computational efficiency of the SBR method fbr adverse Mandarin speech recognition. It differs from the SBR method in using three broad-class sub-codebooks to encode the feature vector of each frame and combining the three encoding residuals to form the frame-level signal bias estimate. A novel approach involving softly combining the board-class encoding residuals using dynamic weighting functions generated by an RNN is applied. Experimental results show that the RRSBR method significantly outperforms the SBR method.
URI: http://dx.doi.org/10.1049/el:19990637
http://hdl.handle.net/11536/31334
ISSN: 0013-5194
DOI: 10.1049/el:19990637
期刊: ELECTRONICS LETTERS
Volume: 35
Issue: 11
起始頁: 875
結束頁: 876
顯示於類別:期刊論文


文件中的檔案:

  1. 000080971500016.pdf