基于小波变换的汉语声调识别新方法

利用小波变换能够模拟人耳听觉特性的优点及其时频局部化特性，对实际语音信号进行分析．根据小波变换模极大值原理以及语音信号的奇异和瞬态特性，提出了一种新的小波变换峰值检测算法．该算法可用于精确提取语音的基音周期，准确进行声调识别．最后，给出应用实例说明了本文算法的有效性．

噪声环境下的汉语声调识别

利用语音信号与噪声信号具有不同相关特性的特点,提出了一种新的加权自相关基频检测算法,该方法可以提高噪声环境下基音检测的准确性。在分类器设计方面,通过引入支持矢量机,进一步提高低信噪比下的汉语声调识别率。实验结果表明,新方法对提高噪声环境下的声调识别效果是十分有效的。

Discriminative tone model training and optimal integration for Mandarin speech recognition

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.