基于多码本矢量量化的非限定文本的 联机话者辨认方法

传统的利用话者的一个时期的语音作为训练语音，进行话者码本训练的方法，识别系统往往不够稳定．为了适应话者自身语音的时变性，文中提出了利用话者不同时期的语音进行训练话者的模型，每个话者具有多个码本．这些码本是采用逐渐减小误识率的优化过程得到的．为了补偿不同信道对系统识别性能的影响，文中给出了一种信道补偿方法．同时提出以一帧高能的浊音语音特征代替一个浊音音素的特征，实现了在线浊音特征提取，利用两级矢量量化及码本索引策略减少了４４％的识别计算量．这些方法大大增加了系统的识别速度和鲁棒性．文中比较了用ＰＬＰ分析和ＬＰＣ倒谱分析进行话者辨认的识别结果．

AN APPROACH BASED ON MULTIPLE VECTOR QUANTIZATION FOR ON-LINE TEXT-INDEPENDENT SPEAKER IDENTIFICATION

The traditional approach for training speaker codebooks only uses one session training speech samples, but the recognition system based on this approach is usually not robust. To adapt to the intraspeaker variations, the paper here introduces an approach for training speaker codebooks using multiple session training speech samples,with every speaker having multiple codebooks. These codebooks are trained based on the minimum recognition error rate.To compensate for the variations arising from transmission conditions, an approach to compensation of the variation presented. To speed up recognition speed, an on line feature extraction method for voiced sounds and two level vector quantization and codebook index strategy are used. These techniques increase the robustness of the speech feature and speed up the training and identification procedure greatly. Finally, the identification results of comparison using the perceptually based linear predictive(PLP) analysis and the LPC cepstrum analysis are given.