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Energy-efficient MFCC extraction architecture in mixed-signal domain for automatic speech recognition

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Published:17 July 2018Publication History

ABSTRACT

This paper proposes a novel processing architecture to extract Mel-Frequency Cepstrum Coefficients (MFCC) for automatic speech recognition. Inspired by the human ear, the energy-efficient analog-domain information processing is adopted to replace the energy-intensive Fourier Transform in conventional digital-domain. Moreover, the proposed architecture extracts the acoustic features in the mixed-signal domain, which significantly reduces the cost of Analog-to-Digital Converter (ADC) and the computational complexity. We carry out the circuit-level simulation based on 180nm CMOS technology, which shows an energy consumption of 2.4 nJ/frame, and a processing speed of 45.79 μs/frame. The proposed architecture achieves 97.2% energy saving and about 6.4x speedup than state of the art. Speech recognition simulation reaches the classification accuracy of 99% using the proposed MFCC features.

References

  1. Jo, Jihyuck, et al., "Energy-Efficient floating-point MFCC extraction architecture for speech recognition systems." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 24.2 (2016): 754--758.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Kou, Haofeng, et al. "Efficient MFCC feature extraction on Graphics Processing Units." CIWSP IET, 2013:1--4.Google ScholarGoogle Scholar
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  1. Energy-efficient MFCC extraction architecture in mixed-signal domain for automatic speech recognition

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    • Published in

      cover image ACM Conferences
      NANOARCH '18: Proceedings of the 14th IEEE/ACM International Symposium on Nanoscale Architectures
      July 2018
      176 pages
      ISBN:9781450358156
      DOI:10.1145/3232195

      Copyright © 2018 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 17 July 2018

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      • short-paper
      • Research
      • Refereed limited

      Acceptance Rates

      NANOARCH '18 Paper Acceptance Rate30of56submissions,54%Overall Acceptance Rate55of87submissions,63%

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