基于SVM的置信度综合方法在语音识别中的应用

SVM-Based Combination of Confidence Measures in Speech Recognition

  • 摘要: 讨论了语音识别中使用支持向量机(support vector machines,SVM)对音子级置信度进行综合的方法.音子级置信度得分采用传统的方法计算而得,并使用SVM对音子级置信度进行综合得到词级的置信度得分.在说话人无关的汉语孤立词识别实验中,使用作者方法比使用传统方法获得的系统等错误率rEER(equal error rates,EER)有明显降低,可以从基线系统的28.14%降低到23.71%,而系统的复杂度仅有小幅度的上升.

     

    Abstract: Discusses the combination of phone-level confidence measures using SVM (support vector machines) in speech recognition. Phone-level confidence scores are computed using transitional method while word-level confidence scores are computed from phone-level confidence scores using support vector machines. Experiments on speaker-independent mandarin isolated word recognition showed that rEER (equal error rates,EER) with the proposed method are lower than those with traditional methods (the rEER dropped from 28.14% in baseline to 23.71% in the proposed method), and the complexity of the system increased only slightly.

     

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