Deterministically annealed design of speech recognizers and its performance on isolated letters
We attack the general problem of HMM-based speech recognizer design, and in particular, the problem of isolated letter recognition in the presence of background noise. The standard design method based on maximum likelihood (ML) is known to perform poorly when applied to isolated letter recognition. The minimum classification error (MCE) approach directly targets the ultimate design criterion and offers substantial improvements over the ML method. However, the standard MCE method relies on gradient descent optimization which is susceptible to shallow local minima traps. We propose to overcome this difficulty with a powerful optimization method based on deterministic annealing (DA). The DA method minimizes a randomized MCE cost subject to a constraint on the level of entropy which is gradually relaxed. It may be derived based on information-theoretic or statistical physics principles. DA has a low implementation complexity and outperforms both standard ML and the gradient descent based MCE algorithm by a factor of 1.5 to 2.0 on the benchmark CSLU spoken letter database. Further, the gains are maintained under a variety of background noise conditions.