Evaluation of an HMM-based feature-compensation method using the AURORA2J speech recognition

摘要

Summary form only given. In this paper, we describe an HMM-based feature compensation method. The proposed method compensates for noise-corrupted features in the MFCC domain using the output probability density functions (pdf) of the hidden Markov models (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 temporally changing noise environment. We evaluated the proposed method based on the AURORA2J database. All the experiments were conducted in a clean condition. The experimental results indicate that the proposed method, combined with cepstral mean subtraction, can achieve a word accuracy of 85.05%. We also show that the proposed method is useful in a transient pulse noise environment.