A general technique termed adaptive labeling is presented for the normalization of the speech signal. In principle, adaptive labeling is applicable to any sequence of feature vectors of a given dimension. It combines the familiar labeling process executed by a vector quantizer with an adaptive renormalization transformation of the feature vectors proposed here. Adaptive labeling is applied to speech recognition, where the particular interest lies in diminishing the degradation of performance that occurs as a result of changes in the signal characteristics following changes in ambient noise and other recording environment conditions or in response to a change in the characteristics of the talker. Results are presented for a series of experiments using soft and loud noises as well as environments in which microphone-to-speaker distances were allowed to vary. A 5000-word vocabulary with isolated word input was used.
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