Analyzing training dependencies and posterior fusion in discriminant classification of apnoea patients based on sustained and connected speech

Blanco Murillo, José Luis; Fernández Pozo, Rubén; Torre Toledano, Doroteo; Caminero Gil, Francisco Javier y Lopez Gonzalo, Eduardo (2011). Analyzing training dependencies and posterior fusion in discriminant classification of apnoea patients based on sustained and connected speech. En: "12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011", 27/08/2011 - 31/08/2011, Florence Italy. pp. 3033-3036.

Descripción

Título: Analyzing training dependencies and posterior fusion in discriminant classification of apnoea patients based on sustained and connected speech
Autor/es:
  • Blanco Murillo, José Luis
  • Fernández Pozo, Rubén
  • Torre Toledano, Doroteo
  • Caminero Gil, Francisco Javier
  • Lopez Gonzalo, Eduardo
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011
Fechas del Evento: 27/08/2011 - 31/08/2011
Lugar del Evento: Florence Italy
Título del Libro: 12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011
Fecha: 2011
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

We present a novel approach using both sustained vowels and connected speech, to detect obstructive sleep apnea (OSA) cases within a homogeneous group of speakers. The proposed scheme is based on state-of-the-art GMM-based classifiers, and acknowledges specifically the way in which acoustic models are trained on standard databases, as well as the complexity of the resulting models and their adaptation to specific data. Our experimental database contains a suitable number of utterances and sustained speech from healthy (i.e control) and OSA Spanish speakers. Finally, a 25.1% relative reduction in classification error is achieved when fusing continuous and sustained speech classifiers. Index Terms: obstructive sleep apnea (OSA), gaussian mixture models (GMMs), background model (BM), classifier fusion.

Más información

ID de Registro: 12940
Identificador DC: http://oa.upm.es/12940/
Identificador OAI: oai:oa.upm.es:12940
Depositado por: Memoria Investigacion
Depositado el: 26 Feb 2013 08:58
Ultima Modificación: 21 Abr 2016 12:15
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