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.

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In  Proc. of the International Conference on Acoustics, Speech and Signal Processing

Publisher  Institute of Electrical and Electronics Engineers, Inc.
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