基于GMM统计参数和SVM的说话人辨认研究

支持向量机(SVM)是以统计学习理论为基础,解决模式识别问题的有力工具,但是它训练算法复杂,难以处理大量样本,限制了其在说话人识别方面的使用。针对这个问题,提出了一种基于GMM(高斯混合模型)统计参数和SVM的说话人辨认系统,以GMM模型的统计参数来训练SVM说话人辨认模型,有效解决了大样本数据下SVM模型的训练问题。实验表明,该方法有良好的效果,并且与倒谱加权方法结合后,可以增强系统的健壮性,进一步提高系统的识别率。

Text-Independent Speaker Identification Based on GMM Statistical Parameters and SVM

Support vector machine(SVM) based on statistical learning theory is a powerful tool for pattern recognition problems.However,since the training algorithm is too complex to accept large quantitise of trainging data,the use for speaker recognition is limited.A system based on Gaussian mixture model(GMM) statistical parameters and SVM for text-independent speaker identification is proposed to aim at this problem.The SVM speaker model is trained from the parameters of GMM,which is an efficient method on large-scale training data.The results of experiments showed that the system has good performance.Especially when combined with cepstral coefficient weighting method,the system's robustness and performance are further improved.