| 汉语语音识别中区分性声调模型及最优集成方法 |
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| 引用本文： | 黄浩,朱杰.汉语语音识别中区分性声调模型及最优集成方法[J].东南大学学报,2007,23(2):174-178. |
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| 作者姓名： | 黄浩 朱杰 |
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| 作者单位： | 上海交通大学电子工程系 上海200240 |
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| 摘 要： | 提出了2种解决汉语语音识别中声调问题的方法:利用区分性方法对基于隐马尔可夫模型(HMM)的声调模型进行训练;提出将区分性训练的声调模型加入大词汇量连续语音识别系统的最优方法,该方法根据最小音子错误的训练准则以及利用扩展Baum-Welch算法区分性训练与模型相关的概率权重,对声学模型以及声调模型概率进行加权.实验结果表明区分性训练的声调模型能够显著地提高连续语音声调识别率以及大词汇量语音识别系统的识别率,同时区分性的模型权重训练能够在区分性声调模型加入连续语音识别系统之后进一步提高系统的识别性能.
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| 关 键 词： | 区分性训练 最小音子错误 声调模型 汉语语音识别 |
| 修稿时间： | 2006-11-06 |
Discriminative tone model training and optimal integration for Mandarin speech recognition |
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| Huang Hao,Zhu Jie.Discriminative tone model training and optimal integration for Mandarin speech recognition[J].Journal of Southeast University(English Edition),2007,23(2):174-178. |
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| Authors: | Huang Hao Zhu Jie |
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| Institution: | Department of Electronic Engineering, Shanghai Jiaotong University, Shanghai 200240, China |
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| Abstract: | Two discriminative methods for solving tone problems in Mandarin speech recognition are presented.First,discriminative training on the HMM (hidden Markov model) based tone models is proposed.Then an integration technique of tone models into a large vocabulary continuous speech recognition system is presented.Discriminative model weight training based on minimum phone error criteria is adopted aiming at optimal integration of the tone models.The extended Baum Welch algorithm is applied to find the model-dependent weights to scale the acoustic scores and tone scores.Experimental results show that tone recognition rates and continuous speech recognition accuracy can be improved by the discriminatively trained tone model.Performance of a large vocabulary continuous Mandarin speech recognition system can be further enhanced by the discriminatively trained weight combinations due to a better interpolation of the given models. |
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| Keywords: | discriminative training minimum phone error tone modeling Mandarin speech recognition |
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