基于HMM／MLFNN混合结构的说话人辨认研究

将隐马尔可夫模型与人工神经网络相结合既利用ＨＭＭ能够较好地描述动态时间序列又ＡＮＮ静态分类能力强的特点，应用于说话人辨认。本文将一个多层前馈神经网络与ＨＭＭ相结合构成混合模模型，与以往的方法不同。具有所需训练数据量小，推广性能良好的特点。

A Hybrid Architecture Based on HMM/MLFNN for Speaker Identification

A hybrid architecture is presented,which is based on Hidden Markov Models (HMM) and Artificial Neural Networks (ANN).The HMM provides a good probabilistic representation for temporal sequences while the ANN has a powerful ability of static classification,so the integration of Multilayer Feed forward Neural Networks (MLFNN) and HMM can give a good hybrid architecture for speaker identification.Unlike most of previous methods of hybrid HMM/MLP,this architecture needs only small amount of data for training and it is also good in generalization.Using this architecture,some experiments of speaker identification have been conducted with better performance than that of single HMM.