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- W2000626086 abstract "Improved acoustic modeling can significantly decrease the error rate in large-vocabulary speech recognition. Our approach to the problem is twofold. We first propose a scheme that optimizes the degree of mixture tying for a given amount of training data and computational resources. Experimental results on the Wall Street Journal (WSJ) Corpus show that this new form of output distribution achieves a 25% reduction in error rate over typical tied-mixture systems. We then show that an additional improvement can be achieved by modeling local time correlation with linear discriminant features." @default.
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- W2000626086 modified "2023-10-17" @default.
- W2000626086 title "High-accuracy large-vocabulary speech recognition using mixture tying and consistency modeling" @default.
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- W2000626086 doi "https://doi.org/10.3115/1075812.1075886" @default.
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