C-based design of a real time speech recognition system

Speech recognition is becoming a popular technology for the implementation of human interfaces. However, conventional approaches to large vocabulary continuous speech recognition require a high performance CPU. In this paper, we describe a speech-recognition system designed using a C-based design methodology and compare three hardware implementations for the computationally intensive parts. Pipelining, parallel processing and cache memory solutions to compute the hidden Markov model (HMM) output probability at high speed were implemented and their performances evaluated. It is shown that designers can rapidly explore a wide range of complex circuits in a using this methodology and that real time speech recognition in small portable systems is possible