GMM-based speaker verification

The vad function MATLAB code for pyBK implements speaker differentiation on a list of audio files by performing speaker binarization (speech segmentation and clustering in multi-speaker scenarios). The system utilizes a binary key background model (KBM), which is trained on conference data, eliminating the need for external training datasets. This results in a system that is easy to operate and adjust for speaker differentiation tasks. Additionally, the implementation includes useful features for the speaker digitization system pipeline. The code was developed and tested in Python 3.6 using conda, relying on common packages for audio processing, feature extraction, and speech activity detection. Installation steps:1. $ conda create -n pyBK python=3.62. $ source activate pyBK3. $ conda install numpy4. $ conda install -c conda-forge librosa5. $ pip install webrtcvad6. $ git clone h...

GMM模型用于语音识别，而AdaBoost用于集成学习，可提升弱学习器的预测精度。

This submission includes useful MATLAB functions for speaker recognition using adapted GMM. The implementation details for steps (i)-(iii) can be found in [1]. The fourth function, gmm2sv.m, connects the means (i.e., centers) of the GMM. The cascade means of the adapted GMM are referred to as the GMM supervector (GSV), which is used in the GMM-SVM based speaker recognition system. More information about the GMM-SVM based speaker recognition system can be found in [2]. These codes require the Netlab toolbox. You can access it at: Netlab Toolbox. References: [1] DA Reynolds, TF Quatieri, and RB Dunn, “Speaker Verification Using Adapted Gaussian Mixture Models,” Digital Signal Processing, Vol. 10, pp. 19–41, 2000. [2] Campbell, W. M.; Sturim, D. E.; Reynolds, D. A.; “Support Vector Machines Using GMM Supervectors for Speaker Verification,” Signal Processing Letters.

该算法采用贪心策略结合EM算法，通过优化数据与模型的匹配度，寻找数据对GMM模型的最佳匹配，从而实现基于模型的聚类。

在语音识别领域，基于MFCC的GMM语音识别matlab源码正在被优化和应用。随着技术进步，这一技术正逐步成为语音处理的重要工具。

这是在Matlab环境下利用高斯混合模型（GMM）进行说话人识别的源程序。训练模型已固定，可稳定运行并生成结果。分享给大家，希望能够帮助到需要的人。

基于重抽样的多重假设方法 [Peter H. Westfall, S. Stanley Young]

高斯混合模型的参数估计通常使用期望最大化（EM）算法，这在matlab环境下尤为常见。

利用Kmeans初始化和Fisher Vectors计算的高效GMM拟合（仅限对角协方差），基于yael包该工具箱可利用BLAS/OpenMP API在多核处理器上实现更快的计算。支持单/双精度的密集输入。

Oracle基于成本的核心原则