This paper proposes a novel method to reduce the order of prediction filter from 10 to 7 in Code Excited Linear Prediction (CELP) coding framework by the inclusion of psychoacoustic Mel scale into Linear Predictive Coding (Mel-LPC). Efficient quantization methods using 2-split Vector Quantization (VQ) for Mel-LPC obtained a reduction of 4 bits/frame and resulted in a total bit gain of 200 bps. A weighting scheme for the Euclidean distance measure gave a reduction of 6 bits/frame that adds up to a total bit gain of 300 bps. A lower Mel-LPC order of 3 has been employed for unvoiced frames by using the perceptual quality as selection criteria and an efficient VQ method using 5 bits is developed which brought down the average bit requirement to 11.5 bits/frame. To incorporate this into Mel-LPC-based CELP encoding scheme, a neural network-based voiced-unvoiced classification algorithm using 5 derived features as input has been constructed and this selection of filter order based on signal statistics provides the benefit of bit reduction by 625 and 325 bps, respectively, for 10th order LPC and 7th order Mel-LPC. In addition to all, the incorporation of Mel-LPC gives a better performance in the estimation of formants.

本文提出了一种通过将心理声学梅尔量表纳入线性预测编码（Mel-LPC）来将代码激励线性预测（CELP）编码框架中的预测滤波器的阶数从10减少到7的新方法。使用针对Mel-LPC的2分割矢量量化（VQ）的高效量化方法，每帧减少了4位，导致总位增益为200 bps。欧几里德距离测度的加权方案减少了6位/帧，从而使总位增益达到300 bps。通过使用感知质量作为选择标准，对清音帧采用了较低的Mel-LPC阶数3，并开发了一种使用5位的有效VQ方法，该方法将平均位要求降低到11.5位/帧。要将其合并到基于Mel-LPC的CELP编码方案中，构造了一个基于神经网络的语音清音分类算法，该算法使用5个派生特征作为输入，并且基于信号统计信息的滤波器阶数选择对于10阶LPC和7阶分别提供了625和325 bps的比特减少优势梅尔-LPC。除此以外，掺入Mel-LPC还可以更好地评估共振峰。