Speech Communication - Eurospeech '91
Speech recognition in noisy environments using first-order vector Taylor series
Speech Communication
Cepstral domain segmental feature vector normalization for noise robust speech recognition
Speech Communication - Special issue on robust speech recognition
A vector Taylor series approach for environment-independent speech recognition
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
Computer Speech and Language
Mechanism of the cross-terms in spectrograms
IEEE Transactions on Signal Processing
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Most compensation methods to improve the robustness of speech recognition systems in noisy environments such as spectral subtraction, CMN, and MVN, rely on the fact that noise and speech spectra are independent. However, the use of limited window in signal processing may introduce a cross-term between them, which deteriorates the speech recognition accuracy. To tackle this problem, we introduce the q-logarithmic (q-log) spectral domain of non-extensive statistics and propose q-log spectral mean normalization (q-LSMN) which is an extension of log spectral mean normalization (LSMN) to this domain. The recognition experiments on a synthesized noisy speech database, the Aurora-2 database, showed that q-LSMN was consistently better than the conventional normalization methods, CMN, LSMN, and MVN. Furthermore, q-LSMN was even more effective when applied to a real noisy environment in the CENSREC-2 database. It significantly outperformed ETSI AFE front-end.