A new architecture is presented to support the general class of real-time large-vocabulary speaker-independent continuous speech recognizers incorporating language models. Many such recognizers require multiple high-performance central processing units (CPU's) as well as high interprocessor communication bandwidth. This array processor provides a peak CPU performance of 2.56 giga-floating point operations per second (GFLOPS) as well as a high-speed communication network. In order to efficiently utilize these resources, algorithms were devised for partitioning speech models for mapping into the array processor. Also, a novel scheme is presented for a functional partitioning of the speech recognizer computations. The recognizer is functionally partitioned into six stages, namely, the linear predictive coding (LPC) based feature extractor, mixture probability computer, (phone) state probability computer, word probability computer, phrase probability computer, and traceback computer. Each of these stages is further subdivided as many times as necessary to fit the individual processing elements (PE's). The functional stages are pipelined and synchronized with the frame rate of the incoming speech signal. This partitioning also allows a multistage stack decoder to be implemented for reduction of computation.
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