Feature-dependent compensation of coders in ARTICLE IN PRESS speech recognition
نویسندگان
چکیده
A solution to the problem of speech recognition with signals corrupted by coders is presented. The coding-decoding distortion is modelled as feature dependent. This model is employed to propose an unsupervised expectationmaximization (EM) estimation algorithm of the coding–decoding distortion that is able to cancel the effect of coders with as few as one adapting utterance. No knowledge about the coder is required. The feature-dependent adaptation can give a word error rate (WER) 21% lower than the feature-independent model. Finally, when compared to the baseline system, the reduction in WER can be as high as 70%.
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