In speech recognition, the speech signal is usually represented in multidimensions but the hidden Markov model (HMM) is one-dimensional. A multichannel HMM (MC-HMM) is proposed as a more robust modeling method for multi-channel signals. Weighting among channels can be incorporated into the model in an uniform way, i.e. both model parameters and weighting coefficients can be estimated by the efficient Baum-Welch training procedure. Moreover, it can be shown that weighting among channels is exactly equivalent to relaxing the probability constraints. Therefore, for the weighting, no extra parameter is actually needed, and consequently no extra memory and computational costs are required. The preliminary experiment results on word spotting show that MC-HMM is better than the standard HMM.
['Dongxin Xu', 'Craig L. Fancourt', 'Chuan Wang']
Multi channel HMM