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Freitas, J., Teixeira, A., Silva, S., Oliveira, C. & Dias, M. S. (2015). Detecting nasal vowels in speech interfaces based on surface electromyography. PLoS One. 10 (6)

J. Freitas et al.,  "Detecting nasal vowels in speech interfaces based on surface electromyography", in PLoS One, vol. 10, no. 6, 2015

@article{freitas2015_1732206745259,
	author = "Freitas, J. and Teixeira, A. and Silva, S. and Oliveira, C. and Dias, M. S.",
	title = "Detecting nasal vowels in speech interfaces based on surface electromyography",
	journal = "PLoS One",
	year = "2015",
	volume = "10",
	number = "6",
	doi = "10.1371/journal.pone.0127040",
	url = "http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0127040#sec014"
}

TY  - JOUR
TI  - Detecting nasal vowels in speech interfaces based on surface electromyography
T2  - PLoS One
VL  - 10
IS  - 6
AU  - Freitas, J.
AU  - Teixeira, A.
AU  - Silva, S.
AU  - Oliveira, C.
AU  - Dias, M. S.
PY  - 2015
SN  - 1932-6203
DO  - 10.1371/journal.pone.0127040
UR  - http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0127040#sec014
AB  - Nasality is a very important characteristic of several languages, European Portuguese being one of them. This paper addresses the challenge of nasality detection in surface electromyography (EMG) based speech interfaces. We explore the existence of useful information about the velum movement and also assess if muscles deeper down in the face and neck region can be measured using surface electrodes, and the best electrode location to do so. The procedure we adopted uses Real-Time Magnetic Resonance Imaging (RT-MRI), collected from a set of speakers, providing a method to interpret EMG data. By ensuring compatible data recording conditions, and proper time alignment between the EMG and the RT-MRI data, we are able to accurately estimate the time when the velum moves and the type of movement when a nasal vowel occurs. The combination of these two sources revealed interesting and distinct characteristics in the EMG signal when a nasal vowel is uttered, which motivated a classification experiment. Overall results of this experiment provide evidence that it is possible to detect velum movement using sensors positioned below the ear, between mastoid process and the mandible, in the upper neck region. In a frame-based classification scenario, error rates as low as 32.5% for all speakers and 23.4% for the best speaker have been achieved, for nasal vowel detection. This outcome stands as an encouraging result, fostering the grounds for deeper exploration of the proposed approach as a promising route to the development of an EMG-based speech interface for languages with strong nasal characteristics.
ER  -