Asynchronous-transition HMM

We propose a new class of hidden Markov model (HMM) called asynchronous-transition HMM (AT-HMM). Opposed to conventional HMMs where hidden state transition occurs simultaneously to all features, the new class of HMM allows state transitions asynchronized between individual features to better model asynchronous timings of acoustic feature changes. In this paper, we focus on a particular class of AT-HMM with sequential constraints based on a novel concept of “state tying along time”. To maximize the advantage of the new model, we also introduce a feature-wise state tying technique. Speaker-dependent speech recognition experiments demonstrated error reduction rates more than 30% and 50% in phoneme and isolated word recognitions, respectively, compared with conventional HMMs