Connected word recognizer on a multiprocessor system

Speech recognition algorithms employing a similarity measure between the input speech utterance and the stored reference patterns to determine recognition of a word/sentence are computationally intensive. The instantaneous vocabulary size that can be handled in real-time is relatively small. This limitation can be alleviated by either using multiple programmable processors or by using special purpose hardware to handle the computation-intensive tasks. In a research environment the former approach is preferred, because improvements to the algorithm can rapidly be incorporated and their effects studied in real-time. Texas Instruments has developed a multiple-processor architecture based on the TMS32020 DSP, called Odyssey, that interfaces with Explorer, a symbolic computer. This paper addresses the issues involved in partitioning and allocating tasks in a multiple-processor environment to maximise throughput, and discusses the implementation of a grammar-driven speaker-dependent connected-word recognizer (GDCWR) as an example application that uses the power of multiple processors.