连续语音识别系统的新语音模型

R. Fagundes, J. S. Correa, P. Dumouchel
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引用次数: 2

摘要

本工作的主要目标是描述一个使用语音语音方法的大词汇量连续语音识别系统的新模型。这项工作提出了一个统计语音结构,应用于语音-语音水平,以提高语音-语音建模系统的语音识别性能。结果表明,一般似然分数增加,表明识别性能较好。这是由于统计语音结构导致语言本身某些频繁的语音组合的增强。这种结构应该被认为是一个额外的知识库,包含有关真实语言语音结构的信息。此外,这种新的语音-音系方法应该被强烈推荐用于自发语音识别系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new phonetic model for continuous speech recognition systems
The main goal of this work is to describe a new model for a large vocabulary continuous speech recognition system using a phonetic-phonological approach. This work proposes a statistical phonetic structure, applied at the phonetic-phonological level, to improve the speech recognition performance in systems with phonetic-phonological modeling. It is shown that the general likelihood scores are increased, indicating better recognition performance. This is due to the fact that the statistical phonetic structure leads to enhancement of some frequent phonetic combinations from the language itself. Such a structure should be considered as an additional knowledge base, containing information about the real language phonetic structure. Also this new phonetic-phonological approach should be strongly recommended for use in spontaneous speech recognition systems.
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