Noise robust Chinese speech recognition using feature vector normalization and higher-order cepstral coefficients

Speaker-dependent, or speaker-trained, isolated word recognition is a key technology behind automatic name dialling systems. In this paper, we investigate how the feature extraction process should be modified so that a maximum recognition rate could be achieved in Chinese name dialling under clean and noisy operating conditions. Our experimental results indicate that the use of higher-order cepstral coefficients improved the recognition rate by 30%. This performance gain is due to the fact that the higher-order cepstral coefficients are expected to carry tonal information. Noise robustness of a system could be improved by integrating the second-order time derivatives in the final feature vector