Parallel Architectures for Speech Recognition: Testing Expensive Algorithms in a Reconfigurable Environment

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Silverman A strong research program in the areas of computing architectures for advanced speech recognition, algorithms, and experimentation is currently in place in the Laboratory for Engineering Man/machine Systems (LEMS) at Brown. This research focuses on a reconfigurable, general-purpose, computing structure, and speech recognition/training algorithms, to advance the state-of-the-art in both areas through their interaction. One feature of these efforts is the evaluation of the effects of changes to the early stages of a speech recognizer. This computationally intensive task will utilize the projected Armstrong III reconfigurable hardware system to accelerate the current training algorithm. A second feature is the incorporation of improvements to the current computational and recognition performance of a talker- independent, connected alphadigit recognizer, database, training, and tools, all developed at Brown. These include microphone-array data acquisition, new signal-processing algorithms, non- parametric modeling, better durational modeling, and new VQ/feature-space methods.