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A Hardware Architecture Design Methodology for Hidden Markov Model Based Recognition Systems Using Parallel Processing
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 1993/06/25
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Digital Signal Processing
hidden markov model, speech recognition, corrective training, parallel processing, array processor, system design,
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This paper presents a hardware architecture design methodology for hidden markov model based recognition systems. With the aim of realizing more advanced and user-friendly systems, an effective architecture has been studied not only for decoding, but also learning to make it possible for the system to adapt itself to the user. Considering real-time decoding and the efficient learning procedures, a bi-directional ring array processor is proposed, that can handle various kinds of data and perform a large number of computations efficiently using parallel processing. With the array architecture, HMM sub-algorithms, the forward-backward and Baum-Welch algorithms for learning and the Viterbi algorithm for decoding, can be performed in a highly parallel manner. The indispensable HMM implementation techniques of scaling, smoothing, and estimation for multiple observations can be also carried out in the array without disturbing the regularity of parallel processing. Based on the array processor, we propose the configuration of a system that can realize all HMM processes including vector quantization. This paper also describes that a high PE utilization efficiency of about 70% to 90% can be achieved for a practical left-to-right type HMMs.