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首页> 外文会议>European Conference on Speech Communication and Technology v.3; 20010903-20010907; Aalborg; DK >Improved Context-Dependent Acoustic Modeling for Continuous Chinese Speech Recognition
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Improved Context-Dependent Acoustic Modeling for Continuous Chinese Speech Recognition

机译：用于连续中文语音识别的改进的上下文相关声学建模

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摘要

This paper describes the new framework of context-dependent (CD) Initial/Final (IF) acoustic modeling using the decision tree based state tying for continuous Chinese speech recognition. The Extended Initial/Final (XIF) set is chosen as the basic speech recognition unit (SRU) set according to the Chinese language characteristics, which outperforms the standard IF set. An adaptive mixture increasing strategy is applied when splitting the single Gaussian into mixed Gaussians in each tied state after the decision tree has been constructed. Our experimental results show that these two improvements are helpful to the acoustic modeling of Chinese speech recognition and that the CD XIF model outperforms the baseline syllable model over 30%.
机译：本文介绍了基于上下文的（CD）初始/最终（IF）声学建模的新框架，该框架使用基于决策树的状态绑定进行连续的中文语音识别。根据中文语言的特点，选择扩展的初始/最终（XIF）集作为基本语音识别单元（SRU）集，其性能优于标准的IF集。在构造决策树之后，在每个联系状态下将单个高斯分解为混合高斯时，将应用自适应混合增加策略。我们的实验结果表明，这两项改进有助于中文语音识别的声学建模，并且CD XIF模型优于基线音节模型超过30％。

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