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Title:Surface electromyography based speech recognition system and development toolkit
Author(s):Chang, Daniel
Advisor(s):Bretl, Timothy W.
Contributor(s):Bretl, Timothy W.
Department / Program:Electrical
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
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.
Issue Date:2010-01-06
Rights Information:Copyright 2009 Daniel Chang
Date Available in IDEALS:2010-01-06
Date Deposited:December 2

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