2026-09-21T02:53:28Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/14720 2023-07-10 col_2142_8888 col_2142_5131 com_2142_8887 com_2142_234 com_2142_5130

Bretl, Timothy W. Bretl, Timothy W. Chang, Daniel 2010-01-06T16:41:53Z 2010-01-06T16:41:53Z 2012-01-07T11:00:08Z 2010-01-06T16:41:53Z 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. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2009-10-20T20:14:47Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Chang_Daniel.pdf: 953971 bytes, checksum: a3723920c553584df11b6a08df773e7a (MD5) Chang_Daniel.doc: 1249792 bytes, checksum: 1f1b649be6533c6d549c242f9c602a6a (MD5) Made available in DSpace on 2010-01-06T16:41:53Z (GMT). No. of bitstreams: 3 license.txt: 4061 bytes, checksum: 85bfad10e88267eb3a795131cf94a52c (MD5) Chang_Daniel.pdf: 953971 bytes, checksum: a3723920c553584df11b6a08df773e7a (MD5) Chang_Daniel.doc: 1249792 bytes, checksum: 1f1b649be6533c6d549c242f9c602a6a (MD5) Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by William Ingram (wingram2@illinois.edu) on 2010-01-06T16:42:21Z Item is restricted until 2012-01-06T16:42:19Z Item reinstated by Sarah Shreeves (sshreeve@illinois.edu) on 2012-01-07T11:00:08Z Item was in collections: Dissertations and Theses - Electrical and Computer Engineering (ID: 446) University of Illinois Dissertations and Theses (ID: 204) No. of bitstreams: 4 license.txt: 4061 bytes, checksum: 85bfad10e88267eb3a795131cf94a52c (MD5) Chang_Daniel.pdf.txt: 83622 bytes, checksum: 3f7c4453b6e9ec5ec284318b4cd4e317 (MD5) Chang_Daniel.pdf: 953971 bytes, checksum: a3723920c553584df11b6a08df773e7a (MD5) Chang_Daniel.doc: 1249792 bytes, checksum: 1f1b649be6533c6d549c242f9c602a6a (MD5) Item released from any restrictions by Sarah Shreeves (sshreeve@illinois.edu) on 2012-01-07T11:00:08Z http://hdl.handle.net/2142/14720 en Copyright 2009 Daniel Chang sEMG speech recognition surface electromyography Surface electromyography based speech recognition system and development toolkit 2009-12 Electrical 1933 Electrical & Computer Engr 1200 University of Illinois at Urbana-Champaign Thesis M.S. MS:Electr & Computer Eng-UIUC 10KS1200MS