篇名 | Study of Associative Cepstral Statistics Normalization Techniques for Robust Speech Recognition in Additive Noise Environmentsept |
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卷期 | 14:1 |
作者 | Tu, Wen-hsiang 、 Hung, Jeih-weih |
頁次 | 105-131 |
關鍵字 | Speech Recognition 、 Codebook 、 Noise-Robust Feature 、 THCI Core |
出刊日期 | 200903 |
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%).