Abstract
In this letter, a new environmental model adaptation method is proposed for robust speech recognition under noisy environments. The proposed method adapts initial acoustic models of a speech recognizer into environmentally matched models by utilizing the histogram equalization technique. Experiments performed on the Aurora noisy environment showed that the proposed technique provides substantial improvement over the baseline speech recognizer trained on the clean speech data.
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