On including temporal constraints in Viterbi alignment for speech recognition in noise

This paper addresses the problem of temporal constraints in the Viterbi algorithm in speaker-dependent and independent tasks. The results here presented suggest that in a speaker-dependent task the introduction of temporal constraints can lead to a high improvement with additive or convolutional noise, the statistical modeling of state durations is not relevant if the max and min state duration restrictions are imposed, and truncated probability densities give better results than a metric previously proposed. Finally, word position dependent and independent temporal restrictions are compared in connected word speech recognition experiments and it is shown that the former leads to better results with the same computational load. However the duration model effect could be much less significant when the acoustic model is optimized and when the training and testing conditions are matched