Speech Enhancement Proposed by a Microphone Array Using EM & HMM Model

Speech enhancement in a real life working environment is a challenging open problem, which remains unsolved after a long research. The main difficulty towards enhancement is different environmental conditions as well as speech enhancement is processed in real time basis. In this paper a new approach towards speech enhancement is proposed using EM algorithm and Hidden markov model (HMM) model. This unique approach gives information about statistical structure of speech signal using speech model. By using this new technique a speech model is parameterized by the coefficient of the reverberation filter and spectra of the sensor noise signal. In new approach EM algorithm and HMM model work together, so that noiseless speech signal is clearly audible user. The main function of EM algorithm is that to estimate Noise parameter from Hidden variable model and develop a Bayes optimal model of the original speech signal, At the same time reduces the magnitude of noise signal. This new technique of speech enhancement is having better solution over the traditional methods like Spectral subtraction, Array processing, Noise cancellation. EM algorithm and HMM model work together so we are getting good values of performance parameter as compare to earlier methods. The important parameter for speech enhancement is signal to noise ratio (SNR). So higher the SNR value, noise is less. And result is enhanced speech signal which is noise free.