文件名称:modelbasedonspectrumprediction
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文章展示了基于高斯混合模型的语音频谱预测方法。频谱预测可能在传包过程中预防丢包这方面起到大作用。期望最大化算法用两倍或三倍的连续语音因素来测试模型。模型被用来设计第一，儿等指令预测量。预测表用频谱分配状态来估计并和一个简单的参考模型对比。最好的预测表得到一个平均频率扭曲值是0.46dB小于参考模型-This paper presents methods for speech spectrum prediction based
on Gaussian mixture models. Spectrum prediction may be useful in a packet transmission system where the sensitivity to packet losses is a major problem.
Models of speech are trained by the Expectation Maximization algorithm using pairs, triples etc. of consecutive cepstral vectors.
The models are used to design first, second etc. order predictors.
The prediction schemes are evaluated using the spectral distortion criterion and compared to a simple reference method. The
best prediction scheme obtains an average spectral distortion that
is 0.46 dB less than for the reference method.
on Gaussian mixture models. Spectrum prediction may be useful in a packet transmission system where the sensitivity to packet losses is a major problem.
Models of speech are trained by the Expectation Maximization algorithm using pairs, triples etc. of consecutive cepstral vectors.
The models are used to design first, second etc. order predictors.
The prediction schemes are evaluated using the spectral distortion criterion and compared to a simple reference method. The
best prediction scheme obtains an average spectral distortion that
is 0.46 dB less than for the reference method.
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