Real-Time Speech Recognition System

PROJECT GOALS SRI and U.C.Berkeley are developing hardware for a real-time implementation of spoken language systems (SLS). Our goal is to develop fast speech recognition algorithms and supporting hardware capable of recognizing continuous speech from a bigram or trigram based 10,000 word vocabulary or a 1,000 to 5,000 word SLS system. RECENT RESULTS The special-purpose system achieves its high computation rate by using special-purpose memories and data paths, and is made up of the following several components: • A special-purpose HMM-board with eight newly designed integrated circuits that does the HMM inner-loop processing to implement the word-recognition algorithms. • An output-distribution board made of off-the-shelf components for computing HMM discrete-density state-output probabilities. • A multi-processor TMS32030 board for computing the statistical language processing. This board has a custom high-speed interface to the HMM-board. • A general-purpose CPU board to perform system control. • A DSP board with A/D convertor for computing the feature extraction. • A Sun workstation for computing the spoken language system database retrieval and human machine interface. • Completed the construction of a working hardware prototype. This prototype has been demonstrated running the Resource Management (RM) task as well as the Airline Travel Information System (ATIS) task. • Began intensive use of the hardware for a real-time Airline Travel Information System (ATIS) task. • Completed the design and construction of a second generation multiprocessor TMS32030 grammar processing board. Testing is currently in progress. • Revised and corrected errors in several of the custom VLSI chips that are used for the HMM word-recognition processor. • Complete the construction and testing of the second generation multiple-processor TMS32030 board with a high I/O bandwidth to interface with the special purpose HMM-board. • Implement multiple types of grammars using this hardware. • Collect data about man-machine speech interactions using the real-time hardware. • Integrate the real-time recognizer into our research to shorten the development cycle for new systems • Evaluate the current architecture to determine the computational and algorithmic bottlenecks. • Deliver a hardware prototype to DARPA.