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Descripción
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| 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 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 over a database including both severe OSA and healthy speakers shows an 85% correct classification rate is achieved by using whole-sentence HMMs, outperforming previous schemes proposed in the literature. | |
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Internacional
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Si |
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DOI
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10.1007%2F978-3-642-35292-8_13 |
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Edición del Libro
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Editorial del Libro
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Springer |
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ISBN
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978-3-642-35291-1 |
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Serie
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Título del Libro
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Advances in Speech and Language Technologies for Iberian Languages |
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Desde página
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121 |
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Hasta página
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128 |