Surface electromyography based speech recognition system and development toolkit

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2010

Author dc:creator

• Chang, Daniel

Contributors dc:contributor

• Bretl, Timothy W.

• sEMG
• speech recognition
• surface electromyography

Statement dc:rights

• Copyright 2009 Daniel Chang

Language dc:language

en

Handle dc:identifier

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

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/14720