Cepstral Statistics Compensation and Normalization Using Online Pseudo Stereo Codebooks for Robust Speech Recognition in Additive Noise Environments

• HUNG Jeih-weih

  Dept. of Electrical Engineering, National Chi Nan University The Institute of Electronics, Information and Communication Engineers

抄録

This paper proposes several cepstral statistics compensation and normalization algorithms which alleviate the effect of additive noise on cepstral features for speech recognition. The algorithms are simple yet efficient noise reduction techniques that use online-constructed pseudo-stereo codebooks to evaluate the statistics in both clean and noisy environments. The process yields transformations for both clean speech cepstra and noise-corrupted speech cepstra, or for noise-corrupted speech cepstra only, so that the statistics of the transformed speech cepstra are similar for both environments. Experimental results show that these codebook-based algorithms can provide significant performance gains compared to results obtained by using conventional utterance-based normalization approaches. The proposed codebook-based cesptral mean and variance normalization (C-CMVN), linear least squares (LLS) and quadratic least squares (QLS) outperform utterance-based CMVN (U-CMVN) by 26.03%, 22.72% and 27.48%, respectively, in relative word error rate reduction for experiments conducted on Test Set A of the Aurora-2 digit database.

収録刊行物

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E91-D (2), 296-311, 2008

  一般社団法人 電子情報通信学会

キーワード

• cepstral statistics compensation
• pseudo stereo codebooks
• linear least squares
• quadratic least squares

詳細情報

• CRID: 1390282679355341696
• NII論文ID: 10026801168
• NII書誌ID: AA10826272
• DOI: 10.1093/ietisy/e91-d.2.296
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E91.D/2/E91.D_2_296/_pdf
• 本文言語コード: en
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可