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Memory Access Optimized VLSI for 5000-Word Continuous Speech Recognition

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Abstract

We have developed a memory access reduced VLSI chip for 5,000 word speaker-independent continuous speech recognition. This chip employs a context-dependent HMM (hidden Markov model) based speech recognition algorithm, and contains parallel and pipelined hardware units for emission probability computation and Viterbi beam search. To maximize the performance, we adopted several memory access reduction techniques such as sub-vector clustering and multi-block processing for the emission probability computation. We also employed a custom DRAM controller for efficient access of consecutive data. Moreover, we analyzed the access pattern of data to minimize the internal SRAM size while maintaining high performance. The experimental results show that the implemented system performs speech recognition 2.4 and 1.8 times faster than real-time utilizing 32-bit DDR SDRAM and SDR SDRAM, respectively.

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Acknowledgements

This work was supported in part by the Human Resource Development Project for IT SoC Architect and the Brain Korea 21 Project. This work was also supported in part by the Korea Science and Engineering Foundation(KOSEF) grant funded by the Korea government(MEST) (No. 0414-20090017).

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Authors and Affiliations

  1. School of Electrical Engineering, Seoul National University, Seoul, 151-744, South Korea

    Kisun You, Young-kyu Choi, Jungwook Choi & Wonyong Sung

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  1. Kisun You
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  2. Young-kyu Choi
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  3. Jungwook Choi
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  4. Wonyong Sung
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Correspondence to Kisun You.

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You, K., Choi, Yk., Choi, J. et al. Memory Access Optimized VLSI for 5000-Word Continuous Speech Recognition. J Sign Process Syst 63, 95–105 (2011). https://doi.org/10.1007/s11265-009-0436-2

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  • DOI: https://doi.org/10.1007/s11265-009-0436-2

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