Asynchronous-Transition HMM for Acoustic Modeling
Shigeki Sagayama · 2000
We propose a new class of hidden Markov model (HMM) which we call Asynchronous-Transition HMM (AT-HMM) to model asynchronous temporal structure of acoustic feature sequences. Conventional HMM models a sequence of feature vectors, while temporally changing patterns of acoustic features do not necessarily synchronize with each other. In this paper, AT-HMMs with and without sequential constraints are discussed. Algorithms for generating context-dependent AT-HMM and for deriving sequentially constrained AT-HMM are provided. A new concept of "state tying across time" is also introduced. Speaker-dependent speech recognition experiments demonstrated error reduction rates of more than 30% and 40% in phoneme and isolated word recognition, respectively, compared with conventional HMMs. 1. INTRODUCTION In conventional Hidden Markov Models (HMMs) for speech recognition, acoustic features of input speech are usually treated as a vector sequence (or, equivalently, vector-quantized code sequences)...