Abstract
Information measures have been used in the context of nonlinear systems presenting abrupt complexity changes and related to nonlinear time series analysis. In this study, complexity measures such as Shannon entropy, q-entropy and their associated divergences have been added to a robust speech recognizer front-end. The method proposed here is tested on continuous speech and compared with a classical mel-cepstral analysis. The recognition degradation has been evaluated in both systems in presence of white and babble noise. The results suggest that complexity measures provide additional valuable information for speech recognition in noisy conditions.
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