Memorias de investigación
Book chapters:
Using HMM to Detect Speakers with Severe Obstructive Sleep Apnoea Syndrome
Year:2012

Research Areas
  • Biomedicine,
  • Processing and signal analysis

Information
Abstract
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.
International
Si
10.1007%2F978-3-642-35292-8_13
Book Edition
Book Publishing
Springer
ISBN
978-3-642-35291-1
Series
Book title
Advances in Speech and Language Technologies for Iberian Languages
From page
121
To page
128
Participants

Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Aplicaciones del Procesado de Señal (GAPS)
  • Departamento: Señales, Sistemas y Radiocomunicaciones