A Parallel Implementation of a Hidden Markov Model with Duration Modeling for Speech Recognition

Abstract

Hidden Markov models (HMMs) are currently the most successful paradigm for speech recognition. Although explicit duration continuous HMMs more accurately model speech than HMMs with implicit duration modeling, the cost of accurate duration modeling is often considered prohibitive. This paper describes a parallel implementation of an HMM with explicit duration modeling for spoken language recognition on the MasPar MP-1. The MP-1 is a fine-grained SIMD architecture with 16384 processing elements (PEs) arranged in a 128 × 128 mesh. By exploiting the massive parallelism of explicit duration HMMs, development and testing is practical even for large amounts of data. The result of this work is a parallel speech recognizer that can train a phone recognizer in real time. We present several extensions that include context dependent modeling, word recognition, and implicit duration HMMs.