TN912.34
提出了一种基于小波变换和HMM模型的ARMA新模型参数，并将它用于A…N的英文字母的识别。小波变换可以高频提供高的频率分辨，在低频提供高的时间分辨率，而ARMA模型则可以改善LPC模型中没有零点的不足。实验结果表明，2个零点，10个极点的ARMA对字母C的识别准确性明显提高。
Some parameters based on wavelet transform and ARMA Model are presented. Wavelet transform provides a high frequency resolution, and the ARMA model is more powerful due to its including of the zeros pole in the model. The experimental result of alphabet of A to N from National Institute of Standard Technology (NIST) database is given. The error rate has been improved, especially C.
李姣军.小波变换和HMM模型在语音识别中的应用[J].重庆大学学报,2001,24(4):61-63.
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