基于神经网络的汉语孤立词语音识别

    Neural Networks Based Phonetic Recognition of Isolated Chinese Phrase

    • 摘要: 研究了基于神经网络的中文孤立词语音识别技术;将时间规整算法与神经网络相结合,组成一个混合级联神经网络语音识别系统.在这个模型中,第一级是时间规整神经网络.其作用是完成时间规整功能,从输入不等长的语音信号特征矢量序列中提取固定长度的特征矢量;然后将这组特征矢量馈入后一级BP网络完成语音识别.利用该方法对小词表汉语孤立词进行语音识别实验,获得了98.25%的正确识别率.实验结果表明,该系统不仅利用神经网络解决了语音识别中的时间规整难题,而且识别性能明显得到改善,识别率和训练速度均优于采用线性时间规整的神经网络语音识别方法.

       

      Abstract: The technique of phonetic recognition of isolated Chinese phrase is studied. By combining the time alignment algorithm with neural network technique, a phonetic recognition system based on mixed cascade neural networks is established. The system is composed of two different neural networks. The former is a time alignment network used to solve the time alignment problem and to extract fixed dimension feature vectors from input speech signal. And the latter is a BP network used as the neural network classifier. In the experiment of phonetic recognition for isolated Chinese phrase, the correct recognition rate of this method is up to 98.25%. The experimental results demonstrate that the method can not only solve commendably the time alignment problem by using a neural network but also improve obviously recognition performance. Compared with other ANN based phonetic recognition systems by using the linear time alignment method, both the correct recognition rate and the training speed of the proposed method are improved.

       

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