Adaptive boosting features for automatic speech recognition

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Abstract

In this paper, we present a method to extract probabilistic acoustic features by using the Adaptive Boosting algorithm (AdaBoost). We build phoneme Gaussian mixture classifiers, and use AdaBoost to enhance the classification performance. The outputs from AdaBoost are the posterior probabilities for each frame given all phonemes. Those posterior features are then used to train a new acoustic model in a similar way as the original features. The gains are obtained when we combine them with the baseline features PLP. Adaboost systems for both Arabic and Mandarin have contributed gains on the final system combination in the GALE evaluation of 2011.

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