搜索资源列表
gmm_creation_mle
- 采用期望最大算法最优化初始高斯混合模型的程序，笔者借用ubm自适应方法，很好的解决了模型不收敛的问题。-expectations largest algorithm using optimization initial Gaussian mixture model procedures, the author borrowed ubm adaptive method, a good model is not the solution c
GMMM-UBM
- 毕业设计论文，相当好的，是语音识别方面的，GMM-UBM方面的--GMMM UBM 广播语种识别
gmm_creation_mle
- 采用期望最大算法最优化初始高斯混合模型的程序，笔者借用ubm自适应方法，很好的解决了模型不收敛的问题。-expectations largest algorithm using optimization initial Gaussian mixture model procedures, the author borrowed ubm adaptive method, a good model is not the solution c
GMMM-UBM
- 毕业设计论文，相当好的，是语音识别方面的，GMM-UBM方面的--GMMM UBM 广播语种识别-Graduation project papers, very good is the voice identification, GMM-UBM area- GMMM UBM Broadcasting Language Recognition
gmmtbx(2)
- GMM工具箱，第二部分。包含GMM建模的各种函数。-GMM toolbox, part II. GMM modeling contains a variety of functions.
GMM
- 混合高斯模型的C++程序，封装成为C++的类，直接调用即可。-gaussian mixture model train code
ubm
- universal background model used in front end feature extraction in speech recognition.
GMM
- Gaussian Mixture Model Simulation Source
GMM
- Source code - create Gaussian Mixture Model in following steps: 1, K-means 2, Expectation-Maxximization 3, GMM Notice: All datapoints are generated randomly and you can config in Config.h-Source code- creat
UBM
- C#画图，线，角，面积以及一些图像的处理-C#draw line，angle，range
GMM
- :高斯混合模型(GMM)是一种经典的说话人识别算法，本文在实现其算法的同时，主要模拟了不同噪声环境情况下高斯混合模型 (GMM)的杭嗓声性能，得到了一些有益结论。 -Gaussian mixture model (GMM) is a classic speaker recognition algorithms, this algorithm at the same time in fulfilling its main simu
ReadHTKParam
- when you want to read some parameters from the HTK macro you trained, just use this source code. it help you read a htk parameter(macro file). But, it can read all parameter type. it just read GMM parameter(1 state, N mi
MFCC-GMM
- 基于MFCC的GMM的说话人识别，是很好的语音处理程序-MFCC of the GMM based speaker recognition, speech processing is a very good program
demo_gmm_ubm
- demonstration of GMM-UBM!
GMM_UBM
- Combine GMM and UBM in speaker recognition
MSR-Identity-Toolkit-v1.0
- 微软研究院的说话人识别工具包，包括GMM-UBM、I-Vector。其中demo_gmm_ubm_artificial.m和demo_ivector_plda_artificial.m为生成模拟特征参数进行训练与识别的教学示例，十分适合初学者学习说话人识别基础算法。具体使用方法请看内部文档。-Microsoft Research s speaker recognition toolkit, including GMM-UBM, I-Ve
gmmtrain_EM
- GMM UBM TRAINING CODE
ubm_adapt
- MATLAB CODE FOR UBM ADAPTATION