摘要
介绍一种基于新型小波听觉滤波器组的语音识别特征提取方法。按照人耳听觉临界频带带宽设计一组新型小波带通滤波器组,并详细计算给出构建新型小波滤波器所需要的尺度参数。采用SDA9000串行信号分析仪进行频谱分析,使用型号为MIC3000 Compact PCI Industrial Computer的LSP设备进行FPGA硬件仿真,使用协同神经网络进行模式识别,建立基于Matlab GUI的仿真界面,与高斯小波滤波器组模型所得仿真结果进行对比,从功率谱图和识别结果上进行分析,证明新型小波滤波器组具有更优的识别率和抗噪性。
This paper introduces a feature extraction method based on a new wavelet filter. At first, the new wavelet＇ s theory is introduced. Then, the new wavelet filter is designed according to the concept of human critical frequency band, and the scale parameter which the new wavelet filter need is given. The SDA9000 is used for spectral analysis, the LSP is applied for FPGA hardware simulation. The SNN （Synergetic Neural Networks） is used in train and recognition, and the Gauss wavelet filter is used to compare with the new wavelet filter. The characteristics of numerical and application for the methods are illustrated by using PC simulation of Maflab GUI. After the analysis of the spectrogram and the recognition result, it is found that the new wavelet filter has higher recognition rate and better robustness than traditional feature.
出处
《计算机与现代化》
2010年第3期111-114,117,共5页
Computer and Modernization
基金
成都信息工程学院科研基金资助项目(CRF200826)
关键词
语音识别
听觉模型
听觉滤波器
临界频带
小波滤波器
speech recognition
auditory model
auditory filter
critical bands
wavelet filter
