Integrating Codebook and Utterance Information in Cepstral Statistics Normalization Techniques for Robust Speech Recognition

Cepstral statistics normalization techniques have been shown to be very successful at improving the noise robustness of speech features. This paper proposes a hybrid-based scheme to achieve a more accurate estimate of the statistical information of features in these techniques. By properly integrating codebook and utterance knowledge, the resulting hybrid-based approach significantly outperforms conventional utterance-based, segment-based and codebook-based approaches in noisy environments. For the Aurora-2 clean-condition training task, the proposed hybrid codebook/segment-based histogram equalization (CS-HEQ) achieves an average recognition accuracy of 90.66%, which is better than utterance-based HEQ (87.62%), segment-based HEQ (85.92%) and codebook-based HEQ (85.29%). Furthermore, the high-performance CS-HEQ can be implemented with a short delay and can thus be applied in real-time online systems.