摘要
为了解决语音信号中帧与帧之间的重叠,提高语音信号的自适应能力,本文提出基于隐马尔可夫(HMM)与遗传算法神经网络改进的语音识别系统.该改进方法主要利用小波神经网络对Mel频率倒谱系数(MFCC)进行训练,然后利用HMM对语音信号进行时序建模,计算出语音对HMM的输出概率的评分,结果作为遗传神经网络的输入,即得语音的分类识别信息.实验结果表明,改进的语音识别系统比单纯的HMM有更好的噪声鲁棒性,提高了语音识别系统的性能.
In order to solve the overlap between frames and improve the self-adaptability of the speech signal, an improved speech recognition system based on hidden Markov model（HMM） and genetic algorithm neural network is proposed in this paper. The major improvement is the adoption of wavelet neural networks in the training of Mel frequency cepstral coefficients（MFCC）. And by using HMM models time series of speech signal, the speech＇s score on the output probability of HMM is calculated. The results will be used as the input of genetic neural network, the information of the speech recognition and classification can then be obtained. The experimental results show that, the improved system has better noise robustness than the pure HMM and the performance of the speech recognition system is also improved
出处
《计算机系统应用》
2016年第1期204-208,共5页
Computer Systems & Applications
关键词
隐马尔可夫模型
神经网络
语音识别
遗传算法
hidden markov model
neural network
speech recognition
genetic algorithm
