Support Vector Machines for Postprocessing of Speech Recognition Hypotheses
نویسندگان
چکیده
In this paper, we introduce an approach to improve the recognition performance of a Hidden Markov Model (HMM) based monophone recognizer using Support Vector Machines (SVMs). We developed and examined a method for re-scoring the HMM recognizer hypotheses by SVMs in a phoneme recognition framework. Compared to a stand-alone HMM system, an improvement of 9.2% was reached on the TIMIT database and 12.8% on the Wallstreet Journal Cambridge database using the hybrid framework.
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تاریخ انتشار 2006