Cepstral correction vector quantizer for speech recognition

摘要

A method for correcting cepstral vectors representative of speech generated in a test environment by use of a vector quantization (VQ) system with a codebook of vectors that was generated using speech and acoustic data from a different (training) environment. The method uses a two-step correction to produce test environment cepstral vectors with reduced non-speech acoustic content. The first correction step subtracts, from the test vector, a coarse correction vector that is computed from an average of test environment cepstral vectors. The second step involves a VQ of the coarsely corrected test vector at each node of the VQ tree. The third step is the addition of a fine correction vector to the coarsely corrected test vector that is generated by subtracting a running (moving) average of the coarsely corrected test vectors associated with the deepest VQ tree node from the VQ vector closest to the coarsely corrected test vector. The method is independent of the means used to generate the cepstral vectors and the corrected output cepstra vectors may be used in various speech processing and classifying systems. The method is adaptable to non-stationary environments.