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Robust Speech Recognition by Normalization of the Acoustic Space

Alex Acero and Richard Stern


In this paper we present several algorithms that increase the robustness of SPHINXth, e CMU continuous-spccch speaker-independent recognition system, by normalizing the acoustic spacc via minimization of the overall VQ distortion. We propose an affme transformation of the cepstrum in which a matrix multiplication performs frequency normalization and a vector addition attempts environment normalization. The algorithms for environment normalization are very efficient and they improve dramatically the recognition accuracy when the system is tested on a microphone othcr from the one on which it was trained. The frequency normalization algorithm applies a different warping of the kquency axis to different speakers and it achieves a 10% decrease in error rate.


Publication typeInproceedings
Published inProc. of the International Conference on Acoustics, Speech and Signal Processing
PublisherInstitute of Electrical and Electronics Engineers, Inc.
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