University of Illinois Urbana-Champaign

Surface electromyography based speech recognition system and development toolkit

Chang, Daniel

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Chang_Daniel.pdf

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https://hdl.handle.net/2142/14720

Description

Title
Surface electromyography based speech recognition system and development toolkit
Author(s)
Chang, Daniel
Issue Date
2010-01-06T16:41:53Z
Director of Research (if dissertation) or Advisor (if thesis)
Bretl, Timothy W.
Doctoral Committee Chair(s)
Bretl, Timothy W.
Department of Study
Electrical
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2010-01-06T16:41:53Z
Keyword(s)
  • sEMG
  • speech recognition
  • surface electromyography
Abstract
This thesis describes the implementation of an automatic speech recognition system based on surface electromyography signals. Data collection was done using a bipolar electrode configuration with a sampling rate of 5.77 kHz. Four feature sets, the short-time Fourier transform (STFT), the dual-tree complex wavelet transform (DTCWT), a non-causal time-domain based (E4-NC), and a causal version of E4-NC (E4-C) were implemented. Classification was performed using a hidden Markov model (HMM). The system implemented was able to achieve an accuracy rate of 74.24% with E4-NC and 61.25% with E4-C. These results are comparable to previously reported results for offline, single session, isolated word recognition. Additional testing was performed on five subjects using E4-C and yielded accuracy rates ranging from 51.8% to 81.88% with an average accuracy rate of 64.9% during offline, single session, isolated word recognition. The E4-C was chosen since it offered the best performance among the causal feature sets and non-causal feature sets cannot be used with real-time online classification. Online classification capabilities were implemented and simulations using the confidence interval (CI) and minimum noise likelihood (MNL) decision rubrics yielded accuracy rates of 77.5% and 72.5%, respectively, during online, single session, isolated word recognition.
Graduation Semester
2009-12
Permalink
http://hdl.handle.net/2142/14720
Copyright and License Information
Copyright 2009 Daniel Chang

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