Nonspecific Speech Recognition Method Based on Composite LVQ1 and LVQ2 Network

摘要：

A novel method of normalization is proposed in this paper, in which the MFCC(Mel frequency Cepstral Coefficient) and MFCC(Difference Mel Frequency Cepstral Coefficient) are sampled equidistantly. For these normalized signals, a new speech recognition based on composite LVQ1(Learning Vector Quantization) network and LVQ2(Improved Learning Vector Quantization) network is presented. First, MFCC and MFCC feature extraction algorithms are introduced, then their coefficients are normalized. The recognition is first to learn coarsely by LVQ1 network and then to learn finely by LVQ2 network. Finally the simulation is given, which shows that the proposed algorithm improves the recognition rates effectively, with shorter training time in comparison with LVQ1 network used alone.

关键词： Speech Recognition Mel Frequency Cepstral Coefficient Neural Network Learning Vector Quantization

作者: Shuling Liang Chaoli Wang Jiaming Du

作者单位: School of Optical-electrical and Computer EngineeringUniversity of Shanghai for Science and Technology, 200093, China

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 2304-2308

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）