For speaker identification, Expectation Maximization Algorithm (EM) is adopted to train speaker dependent model, and afterwards recognize speaker according to Maximum a Posteriori Criterion (MAP).
对于语者辨识，语者特定模型直接用语者的语料借助于期望值最大化算法（EM）来训练，辨识算法采用了最大事后概率法则（MAP）；
These results of the closed-set text-independent speaker identification experiments indicate the proposed model and algorithms improve identification accuracy.
闭集文本自由说话人辨认试验证明了提出的模型及其算法的正确性。
Speaker identification was a biometrics that identify people via their voice, and VQ was the best model in the of speaker recognition because the method has a ability of condensing a lot of data.
矢量量化模型有着可将大量数据进行压缩的特点，因此，在语者识别领域中有很好的应用前景。
应用推荐