一种基于改进CP网络与HMM相结合的混合音素识别方法

提出了一种基于改进对偶传播（ＣＰ）神经网络与隐驰尔可夫模型（ＨＭＭ）相结合的混合音素识别方法．这一方法的特点是用一个具有有指导学习矢量量化（ＬＶＱ）和动态节点分配等特性的改进的ＣＰ网络生成离散ＨＭＭ音素识别系统中的码书。因此，用这一方法构造的混合音素识别系统中的码书实际上是一个由有指导ＬＶＱ算法训练的具有很强分类能力的高性能分类器，这就意味着在用ＨＭＭ对语音信号进行建模之前，由码书产生的观测序列中

A Hybrid Approach for Phoneme Recognition Based on Combination of Improved CP Neural Network and HMM

Proposes a hybrid approach for phoneme recognition based on combination of improved counter propagation (CP) neural network and hidden Markov model (HMM). The characteristic of the approach is that the codebook in a discrete HMM based phoneme recognition system is generated by a modified CP neural network with a few improvements, such as supervised learning vector quantization (LVQ) and dynamic node allocation. Hence, in effect, the codebook in the hybrid phoneme recognition system created through the approach is a high performance classifier with much better discriminating power trained by the supervised LVQ algorithm. It means that before a HMM is used for modeling a speech signal, the observation sequence generated by such a codebook contains highly discriminating information. This will greatly improve the recognition performance of HMM at phoneme level. On the other hand, since the training is done for an improved CP neural network with several new designs, the LVQ learning in the training process can be automatically performed in a supervised mode; learning is accelerated; system convergence is improved; more accurate classification is developed; at the same time, size of codebook is effectively reduced, resulting in the additional advantage of making HMM parameter estimation easier. Finally, through two speaker dependent phoneme recognition experiments, the hybrid approach is compared with the traditional VQ HMM phoneme recognition approach, which uses K means generated codebook. The results show that a correct recognition rate of 98%~99% can be achieved by the hybrid recognition system, and the error rate is 4~6 times lower than that of VQ HMM recognition system using a K means generated codebook of the same size.