A discriminative training method for continuous mixture density HMMs and its implementation to recognize noisy speech
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概要
- 論文の詳細を見る
In this paper, a training method for continuous mixture density HMMs, named optimal discriminative training(ODT), and its implementation for speech recognition in noise are described. ODT is one of corrective learning method, applied to continuous mixture density HMMs, and these HMMs are especially useful for speaker-independent speech recognition. Under noisy environments, the recognition categories are liable to confuse, so by using ODT the improvement of recognition accuracy is more expected. Here, we describe the training algorithm of ODT, and the effects of ODT to improve the robustness for adverse environments by the word recognition experiments in noise.
- 社団法人日本音響学会の論文
著者
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Nakajima Kunio
Kunimune Co. Ltd.
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Mizuta Sinobu
Computer and Information Systems Laboratories,Mitsubishi Electric Corporation
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Nakajima Kunio
Computer and Information Systems Laboratories,Mitsubishi Electric Corporation
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Mizuta Sinobu
Computer And Information Systems Laboratories Mitsubishi Electric Corporation
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- A discriminative training method for continuous mixture density HMMs and its implementation to recognize noisy speech