Noise-robust HMMs based on minimum error classification

The authors compare and contrast the noise-robustness of hidden Markov models (HMMs) trained using a discriminant minimum error classification (MEC) optimization criterion with that of HMMs trained using the conventional maximum likelihood (ML) approach. Isolated word recognition experiments were performed on the ATR 5240 Japanese word database. MEC continuous Gaussian mixture density HMMs trained in a specific noisy environment were found to be more robust to changes in the signal-to-noise ratio than conventional ML HMMs. MEC HMMs trained in various noisy environments were more robust in all environments than conventional ML HMMs.<>