A minimax search algorithm for CDHMM based robust continuous speech recognition

In this paper, we propose a novel implementation of a minimax decision rule for continuous density hidden Markov model based robust speech recognition. By combining the idea of the minimax decision rule with a normal Viterbi search, we derive a recursive minimax search algorithm, where the minimax decision rule is repetitively applied to determine the partial paths during the search procedure. Because of its intrinsic nature of a recursive search, the proposed method can be easily extended to perform contin-uos speech recognition. Experimental results on Japanese isolated digits and TIDIGITS, where the mismatch between training and testing conditions is caused by additive white Gaussian noise, show the viability and e(cid:14)ciency of the proposed minimax search algorithm.