Parallel Architectures for Speech Recognition:  Putting     It All Together
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This support is for research on advanced speech recognition                     algorithms architectures, and experimentation in the Laboratory                 for Engineering Man/Machine Systems (LEMS) at Brown University.                 As a basis for the facility of research, the researchers use                    Armstrong, an 80-node loosely coupled distributed multiprocessor                system, and a hardware/software testbed composed of a linear                    array of microphones for speech acquisition.  Problems addressed                include the investigation of computationally efficient algorithms               for time-dependent modeling of speech analysis, study on new                    algorithms for real-time early-decision recognition of connected                speech, and research on fine-time resolution of hidden Markov and               neural-network representations of highly time-varying phonic                    events.
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