基于长时性特征的音位属性检测方法

提出一种基于长时性信息的音位属性检测方法，该方法通过高、低两层时间延迟神经网络(TDNN)进行实现，低层TDNN在短时特征上进行音位属性的检测，高层TDNN在低层检测结果的基础上，对更长时段上的信息进行融合。实验结果表明，引入长时性特征使得音位属性检测率提升约3%，将音位属性后验概率作为音素识别系统的观测特征，使用长时性特征的识别结果提升约1.7%。

Phonological Attribute Detection Method Based on Long-term Features

A novel phonological attribute detection method based on long-term information is presented.This method is comprised of high-level and low-level Time-delayed Neural Networks(TDNN).The low-level TDNN carries out phonological attribute detection on the basis of short-term features,and the high-level TDNN is based on the low-level output and considering the long-term information,and fully taps the relation between speech signals in time.Experimental results show that,compared by the detection using short-term features,the introduction of phonological attribute based on long-term features improves detection rate with 3%.In addition,this paper puts the phonological attribute in phoneme recognition experiments,the results improveing 1.7% in Hidden Markov Model(HMM)-based speech recognition system.