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篇名
Study of Associative Cepstral Statistics Normalization Techniques for Robust Speech Recognition in Additive Noise Environments
作者 Wen-Hsiang Tu (Wen-Hsiang Tu)Jeih-Weih Hung (Jeih-Weih Hung)
中文摘要
Feature statistics normalization techniques have been shown to be very successful in improving the noise robustness of a speech recognition system. In this paper, we propose an associative scheme in order to obtain a more accurate estimate of the statistical information in these techniques. By properly integrating codebook and utterance knowledge, the resulting associative cepstral mean subtraction (A-CMS), associative cepstral mean and variance normalization (A-CMVN), and associative histogram equalization (A-HEQ) behave significantly better than the conventional utterance-based and codebook-based versions in additive noise environments. For the Aurora-2 clean-condition training task, the new proposed associative histogram equalization (A-HEQ) provides an average recognition accuracy of 90.69%, which is better than utterance-based HEQ (87.67%) and codebook-based HEQ (86.00%).
起訖頁 105-131
關鍵詞 Speech RecognitionNoise-Robust FeatureCodebook
刊名 中文計算語言學期刊  
期數 200903 (14:1期)
出版單位 中華民國計算語言學學會
該期刊-上一篇 Summarization Assistant for News Brief Services on Cellular Phones
 

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