Abstract
Nowadays definitive diagnosis of obstructive sleep apnoea (OSA) syndrome is expensive and time-consuming. Previous research on voice characteristics of OSA patients has shown that resonance, phonation and articulation differences arise when compared to healthy subjects. In this contribution we study different speech modeling techniques to detect patients with severe OSA envisioning the future classification of patients according to their priority of need identifying the most severe cases and reducing medical costs.
Hidden Markov Models (HMMs) are used, as generally applied in text-dependent speech recognition, for detecting voices of OSA patients. Specific acoustic properties of continuous speech are modeled attending to different linguistic contexts which reflect discriminative physiological characteristics found in OSA patients. Experimental results on the discrimination of apnoea voices are presented over a database including both severe OSA and healthy speakers. An 85% correct classification rate is achieved by using whole-sentence HMMs, outperforming previous schemes proposed in the literature.
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Benavides, A.M., Blanco, J.L., Fernández, A., Pozo, R.F., Toledano, D.T., Gómez, L.H. (2012). Using HMM to Detect Speakers with Severe Obstructive Sleep Apnoea Syndrome. In: Torre Toledano, D., et al. Advances in Speech and Language Technologies for Iberian Languages. Communications in Computer and Information Science, vol 328. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35292-8_13
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DOI: https://doi.org/10.1007/978-3-642-35292-8_13
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