Surface electromyography based speech recognition system and development toolkit

sEMG;speech recognition;surface electromyography

Chang, Daniel ; Bretl, Timothy W. ; Bretl ; Timothy W.

关键词: sEMG;    speech recognition;    surface electromyography;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/14720/Chang_Daniel.pdf?sequence=2&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

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.