基于深度神经网络的关键词识别系统
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Keyword Recognition System Based on Deep Neural Network
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    摘要:

    针对当前关键词识别少资源或零资源场景下的要求,提出一种基于音频自动分割技术和深度神经网络的关键词识别算法.首先采用一种基于度量距离的改进型语音分割算法,将连续语音流分割成孤立音节,再将音节细分成和音素状态联系的短时音频片段,分割后的音频片段具有段间特征差异大,段内特征方差小的特点.接着利用一种改进的矢量量化方法对音频片段的状态特征进行编码,实现了关键词集内词的高精度量化编码和集外词的低精度量化编码.最后以音节为识别单位,采用压缩的状态转移矩阵作为音节的整体特征,送入深度神经网络进行语音识别.仿真结果表明,该算法能从自然语音流中较为准确地识别出多个特定关键词,算法易于理解、训练简便,且具有较好的鲁棒性.

    Abstract:

    A new algorithm for keyword recognition based on audio automatic segmentation and depth neural network is proposed to identify the requirements of keyword recognition on the condition of low or zero resource. Firstly, an improved speech segmentation algorithm based on metric distance is used to divide the continuous speech stream into isolated syllables, and then the syllable is subdivided into short audio segments which are connected with the phoneme state. The segmented audio segment has the characteristics of large difference between the segments, and the characteristic variance of the segment is small. Then, an improved vector quantization method is used to encode the state features of the audio fragments, and the high precision quantization coding and the low precision quantization coding of the words are realized. Finally, the syllable is used as the recognition unit, and the compressed state transition matrix is used as the whole feature of the syllable. It is sent into the deep neural network for speech recognition. The simulation results show that the algorithm can identify many specific keywords from the natural speech stream, and the algorithm is easy to understand, the training is simple and the robustness is better.

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孙彦楠,夏秀渝.基于深度神经网络的关键词识别系统.计算机系统应用,2018,27(5):41-48

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  • 收稿日期:2017-09-11
  • 最后修改日期:2017-09-30
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  • 在线发布日期: 2018-03-12
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