BBN real-time speech recognition demonstrations

Abstract

Typically, real-time speech recognition -if achieved at all -is accomplished either by greatly simplifying the processing to be done, or by the use of special-purpose hardware. Each of these approaches has obvious problems. The former results in a substantial loss in accuracy, while the latter often results in obsolete hardware being developed at great expense and delay. Starting in 1990 [1] [2] we have taken a different approach based on modifying the algorithms to provide increased speed without loss in accuracy. Our goal has been to use commercially available off-the-shelf (COTS) hardware to perform speech recognition. Initially, this meant using workstations with powerful but standard signal processing boards acting as accelerators. However, even these signal processing boards have two significant disadvantages:1. They often cost as much as the workstation they are plugged into.. The interface between each board and workstation is complicated, and always different for each combination of workstation and board.To make speech recognition available to a broad base of users at an affordable cost, we have eliminated these disadvantages by developing algorithms that are able to operate in real-time on COTS workstations without requiring additional add-on hardware and without decreasing recognition speed and accuracy. An additional advantage is that we are able to benefit from the improvements in workstation price and performance, with very minimal porting effort. The BBN RUBY TM system, a robust commercialization of the BYBLOS TM speech recognition technology, is the result of this development effort. At the workshop, we demonstrated two example systems that employ the RUBY speech recognition system.Both demonstrations run on Silicon Graphics workstations (Personal IRIS 4D/35 and Indigo), which contain a builtin programmable A/D-D/A. The signal processing and vector quantization, which runs in a separate process from the recognition search, communicates with the recognition search via network sockets. We have reduced the computation required for this front end processing to the point where it requires little enough of the CPU so that there is enough left over to perform the more expensive search in real time.Since accuracy is our primary concern, we have verified that this signal processing results in the same accuracy as our previous signal processing software. REAL-TIME ATIS SYSTEMThe ATIS demonstration integrated BBN's DELPHI natural language understanding system with the RUBY speech recognition component. RUBY is used as a black-box, controlled entirely through an application programmers interface (API). The natural language component is our current research system, which runs as a separate process. Both processes run on the same processor, although not at the same time. The NL processing is performed strictly after the speech recognition, since competing for the same processor could not make it faster. (If two separate processors are available, the processing can be overlapped as describedThe speech recognit...