Parallel Architectures for Speech Recognition: Nonlinear Optimization of Expectation-Maximization (EM) Training of Hidden Markov Models (HMMs) in a Reconfigurable Environment

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This research involves the development, construction and testing of new architectures for high-speed computing. The idea being exploited is that of combining in a loosely-coupled fashion, general-purpose, RISC-based processing nodes each with multiple field-programmable-logic-array(FPLA) based coprocessor systems. With this kind of system, one has the hardware properties of a general-purpose computer, but the advantage, brought about by suitable reconfigurability, of performance more akin to that of a special-purpose engine. In particular, the 20-node Armstrong III system has been built and is operational and provides nearly two-orders of magnitude improvement over current advanced workstations. The hardware system, combined with its configuration compiler, a program that automatically translates a C function subroutine to both machine code and to the hardware design of the reconfigurable coprocessors, are the basic building blocks for this class of architectures. Thus, this research is directly focused on one of the strategic areas of national concern, that of high performance computing. There is uniqueness to this program in that not only has the hardware/software system been built, but it is also being tested on important, real applications. In particular, the training of a modern hidden Markov model(HMM) based speech recognition system requires hundreds of hours, even with some of the variants developed recently at Brown and elsewhere. The reduction of this training time to ten or so minutes means that research progress in this area may be made at a faster rate and/or that expensive nonlinear optimization techniques may now be applied to the problem. Also, this means that data from a less cumbersome sensor system (the microphone array systems in place and being developed at Brown) can be suitably incorporated into a more robust speech recognition system. Currently, eight PhD students, two ScM students and five undergraduates are involved with this project; ni ne PhD's have been graduated from this project under past funding.