HMM-Based Speech Enhancement using Explicit Gain Modeling

We propose a hidden Markov model (HMM) based speech enhancement method using explicit modeling of speech and noise gains. The gains are considered to be stochastic variables in an HMM framework. The speech gain models the energy variations of speech phones, typically due to differences in pronunciation and/or different vocalizations of individual speakers. The noise gain helps to improve the tracking of the time-varying energy of non-stationary noise. The time-varying parameters of the gain models are estimated on-line using the recursive expectation maximization (EM) algorithm. The performance of the proposed enhancement system is evaluated through both objective and subjective tests. The experimental results confirm the advantage of explicit gain modeling, particularly for non-stationary noise sources