Implementation of a hidden Markov model-based layered grammar recognizer

The authors describe the challenges encountered and the tradeoffs made in the implementation of a hidden Markov-model-based continuous-word recognizer. Calypso, a multiple processor system, is the implementation platform. The model-driven approach used in the recognition scheme is computationally demanding and requires a scoring buffer of several hundred kilobytes of data memory. Strategies to reduce this need for a large data-memory and number of compute-cycles without adversely impacting performance are given. Fixed-point representation issues and techniques to prevent overflows are addressed. As a result of these studies a TMS320C25-based continuous-word recognizer has been realized. All recognizer functions, including signal processing, model evaluation, and grammar control, are performed by a single TMS320C25. A time/space analysis of the implementation and performance of the word recognizer is given