The Research Of Stuttered Speech Recognition Based On Ann And Hmm

Stuttering is a speech disease, with the development of artificial intelligence, computer popularity and intelligent medical, automatic stuttering recognition has significance.Based on speech recognition and the characteristics of stuttering speech, this paper extracts feature parameters, and builds the artificial neural network (ANN) and hidden Markov model (HMM) to realize automatic stuttering recognition. First, this paper introduces the basis of speech recognition, the current developments and difficult in speech recognition, then introduces the details of the classification process method for identifying stuttering. The stuttering speech database in this paper includes blocked speech, repetition speech, prolonged speech and fluent speech. According to the current research this paper takes two manual cutting methods to get stuttering speech, preprocesses speech which including pre-emphasis, stability framing, and then combining the language model and acoustic characteristics of stuttering, extracts spectral envelope features LPCC as parameters and takes two means which are gray relational analysis and the uniform dividing method to structure it. This paper detailed discusses the analysis and design of applying ANN and HMM to recognize stuttering speech. When using three-layer perception neural network we choose back propagation algorithm for network training and recognition. When using continuous and left to right HMM to identify stuttering, we should establish four stuttering models for different types, each model has six states. Baum-Welch algorithm is applied to train HMM, K-means algorithm is conducted to train the observation probability distribution of HMM parameters. Finally we use Viterbi algorithm to identify stuttering classification.Finally, we had experiments, analyzed the experiments results, the result indicates that the experiments have good data classification ability and recognition ability. In the end of this paper summed up shortcomings and problems of experiments and future prospects for the stuttering recognition development.