基于HMM/SVM两级结构的汉语易混淆语音识别

Confusable Chinese Speech Recognition Based on HMM/SVM Two-Level Architecture

基于 HMM 的汉语语音识别中,易混淆语音的识别率仍然不高.在分析 HMM 固有缺陷的基础上,本文提出一种使用 SVM 在 HMM 系统上进行二次识别来提高易混淆语音识别率的方法.通过引入置信度估计环节,提高系统性能和效率.通过充分利用 Viterbi 解码获得的信息来构造新的分类特征,从而解决标准 SVM 难以处理可变长数据的问题.详细探讨这种两级识别结构中置信度估计、分类特征提取和 SVM 识别器构造等问题.语音识别实验的结果显示,与采用 HMM/SVM 混合结构的模型相比,本文方法在对识别速度影响很小的情况下可以使识别率有明显提高.这表明所提出的具有置信估计环节的 HMM/SVM 两级结构用于易混淆语音识别是可行的.

The recognition rate for confusable speech is still low in state-of-the-art Chinese speech recognition systems based on HMM. The inherent defects of HMM are analyzed, then a two-level-architecture recognition framework combining HMM and SVM is proposed. A confidence estimation module is adopted to improve the performance and efficiency of the system . The information obtained by Viterbi decoding is utilized to construct new classes of feature for SVM, which solves the problem that the conventional SVM cannot directly process variable length sequences. The relevant issues, such as confidence estimation, classification feature extraction and SVM recognizer construction, are addressed. The experimental results of confusable Chinese speech show that compared with the hybrid HMM/SVM based system the proposed method can highly improve the recognition rate with little impact on the running speed.