A Study Of Voice Activity Detection Algorithm Based On HMM/SVM

Voice activity detection is a technology for each speech and non-speech segments technology. It is voice activity detection by the detection of speech and noise, and different processing methods for different signals. Have a vital role to improve the communication service quality and efficiency of communication. Research for voice activity detection algorithm is very necessary, it can save the resource of speech channel without reducing quality of communication, also it’s an important part of the IP phone application. Not only should it save the valuable bandwidth but also lower the feeling of the end-to-end delay. It has great theoretical and practical significance and broad application prospects.This paper analysis the basic theory, discusses the framework of the voice activity detection algorithms based on HMM. First introduced the basic concept of HMM, then discuss the algorithm framework, namely HMM three big problems namely evaluation, estimation, model parameter optimization problem, and then discusses the applicability of HMM model, the speech signal in speech energy properties also includes pitch, duration, lexical and language information structure, then analyzes the key the problem of applying the HMM model on voice activity detection, puts forward the basic algorithm for voice activity detection based on HMM, introduces the voice activity detection algorithms based on HMM, which include feature extraction, model training, voice activity detection decision this several stages.Secondly, the paper puts forward the design framework of HMM/SVM detection algorithm based on voice activity. The method of combining hidden Markov model and support vector machine, using HMM to model the speech signal, and use the Viterbi decoding algorithm to decode the speech signal input, converts speech signals decoded as the feature vector SVM, and the feature vectors are input into the SVM classifier for classification and discrimination. This method can overcome their shortcomings, to play their respective advantages, to achieve the good results.Finally, the paper analyzes test and application, sampling of the actual engineering noise environment, the kernel function and VAD algorithm performance comparison. Get the RBF kernel compared to other kernel function has more stable performance in the nuclear function in experiment, have good learning ability, low error at low SNR, promotion of more good than environment statistics; see the new algorithm generally better than traditional VAD performance analysis.