Recognition of speech from signals secondary to speech

Hartzog, S.; Morse, Michael S.; Trull, B.; Alegre, C.; Harris, Paul D.

doi:10.1109/iembs.1988.94868

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…ABD is a muscle capable for the forward and backward movement of the tongue and also responsible for the movement of the hyoid bone inside the neck. This supports the fact that the movement of tongue and the hyoid bone is different for male and female [21,22]. This fact is also true, even when the speech is silently spoken [21].…”

Section: Resultssupporting

confidence: 72%

Feasibility of facial EMG in gender classification during speech production

Godiyal

Sharma

Joshi

et al. 2012

Journal of Medical Engineering & Technology

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Gender identification has its unique importance in sports and forensic sciences. However, the social issues are major constraints to identify the gender of a human being. A novel approach for gender classification using facial EMG is presented. The time domain features of facial EMGs are explored towards their robustness in gender identification. A Davies-Bouldin index is calculated to evaluate the features. Anterior belly of digastrics along with Orbicularis Oris Inferior showed the promising classification accuracy of 80% with the reported features. A possible enhancement in accuracy could be possible by a larger dataset.

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Section: Resultssupporting

confidence: 72%

Feasibility of facial EMG in gender classification during speech production

Godiyal

Sharma

Joshi

et al. 2012

Journal of Medical Engineering & Technology

View full text Add to dashboard Cite

show abstract

“…The correlation between the MES and speech was observed by Morse et al [11], The MES around the neck was collected during speech using four electrodes. The average amplitude from each channel was then used as an input into a maximum likelihood (ML)…”

Section: Speech Information In Myoelectric Signalsmentioning

confidence: 88%

“…0 ~ 10 mV), signal amplification is required to meet the input ranges of analog to digital converter. 11 The amplified signal is fed into a filter to remove various electrical noises, including 60 Hz power line interference. An analog to digital converter samples and digitalizes the MES, which is the used by the PC for further processing.…”

Section: Figure 2-2 a Typical Mes Acquisition Systemmentioning

confidence: 99%

Augmented speech recognition

Guo¹

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A proposed control scheme for a vocal prosthesis

Day

Morse

Day

IEEE Proceedings on Southeastcon

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Research has been conducted into the recognition of speech utilizing the myoelectric signals exhibited by the muscles that shape the vocal tract.Myoelectric signals from four muscles sites were digitized along with the acoustic waveform. Energy, average magnitude, and standard deviation of the biopotentials served as inputs to a maximum likelihood classifier. Recognition is close to three times apriori for a ten word set with a 7 0 4 0 % chance of recognition within the first five choices of the maximum likelihood algorithm. ACKNOWLEDGEMENTResearch has been done on a control scheme that attempts to recognize speech from the myoelectric signals (MES) exhibited by the muscles that help shape the vocal tract. In this study, the myoelectric signals from four muscle sites were digitized along with the acoustic waveform. A maximum likelihood pattern classifier was applied to three parameters of each MES: energy, average magnitude, and standard deviation. The results thus far yield several indications, including a time-variance of the MES. The goal of the current research is to obtain the best possible recognition in an attempt to approach a clinically acceptable level of accuracy (approaching 95%). REVIEW

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Recognition of speech from signals secondary to speech

Cited by 3 publications

References 3 publications

Feasibility of facial EMG in gender classification during speech production

Feasibility of facial EMG in gender classification during speech production

Augmented speech recognition

A proposed control scheme for a vocal prosthesis

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