RT Journal Article
T1 Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques
A1 Fernández Pozo, Rubén
A1 Blanco Murillo, Jose L
A1 Hernández Gómez, Luis
A1 López Gonzalo, Eduardo
A1 Alcázar Ramírez, José
A1 Toledano, Doroteo T
K1 Programa informático para el reconocimiento del lenguaje hablado
K1 Apnea del sueño obstructiva
K1 Distribución normal
K1 Trastornos de la voz
AB This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.
PB Hindawi Publishing Corporation
SN 1687-6172
YR 2009
FD 2009-06-14
LK http://hdl.handle.net/10668/573
UL http://hdl.handle.net/10668/573
LA en
NO Fernández Pozo R, Blanco Murillo JL, Hernández Gómez L, López Gonzalo E, Alcázar Ramírez J , Toledano DT. Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques. EURASIP J Adv Signal Process. 2009:982531
NO This article is part of the series Analysis and Signal Processing of Oesophageal and Pathological Voices.
DS RISalud
RD Apr 6, 2025