A VLSI Architecture with Multiple Fast Store-Based Block Parallel Processing for Output Probability and Likelihood Score Computations in HMM-Based Isolated Word Recognition

Kazuhiro NAKAMURA  Ryo SHIMAZAKI  Masatoshi YAMAMOTO  Kazuyoshi TAKAGI  Naofumi TAKAGI  

IEICE TRANSACTIONS on Electronics   Vol.E95-C   No.4   pp.456-467
Publication Date: 2012/04/01
Online ISSN: 1745-1353
DOI: 10.1587/transele.E95.C.456
Print ISSN: 0916-8516
Type of Manuscript: Special Section PAPER (Special Section on Solid-State Circuit Design – Architecture, Circuit, Device and Design Methodology)
speech recognition,  hidden Markov model (HMM),  VLSI architecture,  isolated word recognition,  

Full Text: PDF>>
Buy this Article

This paper presents a memory-efficient VLSI architecture for output probability computations (OPCs) of continuous hidden Markov models (HMMs) and likelihood score computations (LSCs). These computations are the most time consuming part of HMM-based isolated word recognition systems. We demonstrate multiple fast store-based block parallel processing (MultipleFastStoreBPP) for OPCs and LSCs and present a VLSI architecture that supports it. Compared with conventional fast store-based block parallel processing (FastStoreBPP) and stream-based block parallel processing (StreamBPP) architectures, the proposed architecture requires fewer registers and less processing time. The processing elements (PEs) used in the FastStoreBPP and StreamBPP architectures are identical to those used in the MultipleFastStoreBPP architecture. From a VLSI architectural viewpoint, a comparison shows that the proposed architecture is an improvement over the others, through efficient use of PEs and registers for storing input feature vectors.