HMM-based noise-robust feature compensation

In this paper, we describe a hidden Markov model (HMM)-based feature-compensation method. The proposed method compensates for noise-corrupted speech features in the mel-frequency cepstral coefficient (MFCC) domain using the output probability density functions (pdfs) of the HMM. In compensating the features, the output pdfs are adaptively weighted according to forward path probabilities. Because of this, the proposed method can minimize degradation of feature-compensation accuracy due to a temporarily changing noise environment. We evaluated the proposed method based on the AURORA2 database. All the experiments were conducted under clean conditions. The experimental results indicate that the proposed method, combined with cepstral mean subtraction, can achieve a word accuracy of 87.64%. We also show that the proposed method is useful in a transient pulse noise environment.