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- W2135688430 abstract "Multiple-model speech recognizer has been shown to be quite successful in noisy speech recognition. However, its performance has usually been tested using the general speech front-ends which do not incorporate any noise adaptive algorithms. For the accurate evaluation of the effectiveness of the multiple-model frame in noisy speech recognition, we used the state-of-the-art front-ends and compared its performance with the well-known multi-style training method. In addition, we improved the multiple-model speech recognizer by employing N-best reference HMMs for interpolation and using multiple SNR levels for training each of the reference HMM." @default.
- W2135688430 created "2016-06-24" @default.
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- W2135688430 date "2010-01-01" @default.
- W2135688430 modified "2023-10-18" @default.
- W2135688430 title "Performance Improvement in Multiple-Model Speech Recognizer under Noisy Environments" @default.
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- W2135688430 doi "https://doi.org/10.1007/978-3-642-14980-1_43" @default.
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