分段模型在解码假设检验中的应用

本文主要研究了分段模型(以参数轨迹模型为例)在解码假设检验中的应用。分段模型与传统的HMM相比,具有更加精确的建模能力。多年来人们一直致力于研究它对语音识别性能的提高,而忽视了其它方面的应用。本文提出了分段模型校验的方法,对HMM的识别结果进行二次处理,克服了传统方法在不同句子间不具有可比性的缺点,简单而有效;在此基础上,为了满足系统的特殊要求,训练Fisher分类器,选择分段模型而非HMM的N-Best信息作为特征输入,验证了分段模型得分作为可信度指标时的优秀区分能力。实验结果表明,在第一类错误率为5%的情况下,最好的第二类错误率可以降到25.265%。这体现了系统良好的拒识性能。

The Application of Segment Models in Hypothesis Testing

This paper introduced the application of Segment Models(SM) in hypothesis testing Compared with HMM, SM relaxes the assumption of the independence of frame features, and thus is powerful in the more precise modeling For decades researchers are engaged in its use for recognition accuracy, but the other fields are rarely dealt with This paper mainly investigates the SM verification (e g Parametric Trajectory Model) in hypothesis testing-alternative PTM provides confidence measurement for HMM result, which is simple but effective On the basis of this, for the special requirement of the acceptance/rejection, Fisher classifier is then used Here SM N Best as input features are proved superior to the HMMs Compared with the traditional methods, the new SM verification achieves excellent performance