Speech enhancement based on hidden Markov model with discrete cosine transform coefficients using Laplace and Gaussian distributions

This paper presents a novel HMM-based speech enhancement framework based on Laplace and Gaussian distributions in DCT domain. We propose analytical procedures for training clean speech and noise models with the aim of Baum´s auxiliary function and present two MMSE estimators based on Gaussian-Gaussian (for clean speech and noise respectively) and Laplace-Gaussian combinations in the HMM framework. The performance evaluation is done using SNR and PESQ measures and the results of the proposed techniques are compared with AR-HMM approach. Higher SNR improvement is achieved for the proposed method in the Gaussian-Gaussian case in comparison with AR-HMM and Laplace-Gaussian techniques for both nonstationary and stationary noises. A similar result is obtained in term of PESQ in the presence of nonstationary noise types.