In algorithm ways, Gaussian mixture model (GMM) is the most successful speaker recognition model at present.
在算法方面，高斯混合模型(GMM)是目前最成功的一种说话人识别模型。
The traditional training methods of Gaussian Mixture Model(GMM) are sensitive to the initial model parameters, which often leads to a local optimal parameter in practice.
为了解决传统高斯混合模型（GMM）对初值敏感，在实际训练中极易得到局部最优参数的问题，提出了一种采用微粒群算法优化GMM参数的新方法。
Gaussian mixture model (GMM) has been widely used for text-independent speaker recognition. This method has simple and efficient character.
高斯混合模型(GMM)已广泛地应用于文本无关的说话人识别系统，该方法具有简单高效的特点。
This feature vector made the Gaussian Mixture Model (GMM) classifier outperform MFCC and Differential MFCC features in classification.
该混合特征使得高斯混合模型(GMM)分类器可获得比使用MFCC特征及其差分MFCC更好的分类性能。
This feature vector made the Gaussian Mixture Model (GMM) classifier outperform MFCC and Differential MFCC features in classification.
该混合特征使得高斯混合模型(GMM)分类器可获得比使用MFCC特征及其差分MFCC更好的分类性能。
应用推荐