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Auditory pathways constitute the complex neural circuits responsible for transmitting and interpreting auditory information from the peripheral auditory system to the brain. Sound waves are initially captured by the outer ear, funneled through the ear canal, and reach the tympanic membrane (eardrum). These vibrations are transmitted via the middle ear's ossicles to the inner ear's cochlea.
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The auditory system is essential for sound perception, utilizing various critical structures. When sound waves enter the outer ear, they travel through the ear canal and cause the eardrum to vibrate. These vibrations are then transmitted to the middle ear, where three tiny bones – the malleus, incus, and stapes – amplify the sound. This amplification is crucial, as it ensures that the sound vibrations are strong enough to be conveyed to the inner ear. These vibrations then reach the...
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Updated: Jan 18, 2026

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Enfoque de Extracción de Características Auditivas para el Reconocimiento Robusto de Voz Patológica

Youssef Zouhir1, Mohamed Zarka1, Lilia El Amraoui2

  • 1Research Laboratory Smart Electricity & ICT, SE&ICT Lab, LR18ES44, National Engineering School of Carthage, University of Carthage, Tunis, Tunisia.

Journal of voice : official journal of the Voice Foundation
|January 15, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Un novedoso método de Extracción de Características Auditivas (AFE) que utiliza un Gammachirp FilterBank mejora significativamente el reconocimiento de voz patológica. Este enfoque logra una alta precisión en la clasificación de trastornos de la voz, superando a los métodos existentes para un mejor cribado clínico.

Palabras clave:
AFEExtracción de características auditivasBanco de filtros auditivosModelo coclearExtracción de característicasBanco de filtros Gammachirp

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Área de la Ciencia:

  • Ingeniería Biomédica
  • Procesamiento de Señales
  • Ciencia del Habla

Sus antecedentes:

  • La clasificación binaria de voz ofrece una utilidad clínica limitada para distinguir tipos específicos de patología.
  • La clasificación multiclase precisa es esencial para el reconocimiento eficaz de voz patológica (PVR).
  • Los métodos de extracción de características existentes a menudo no logran capturar los matices de las voces patológicas.

Objetivo del estudio:

  • Introducir un novedoso enfoque de Extracción de Características Auditivas (AFE) para el reconocimiento multiclase robusto de voz patológica (PVR).
  • Simular la percepción auditiva humana utilizando un Gammachirp FilterBank (GCFB) para la extracción mejorada de características de voz.
  • Evaluar el rendimiento del enfoque AFE frente a los métodos de vanguardia en conjuntos de datos de referencia.

Principales métodos:

  • Se desarrolló un enfoque AFE que emplea un Gammachirp FilterBank (GCFB) con 128 filtros, simulando el comportamiento espectral coclear.
  • Se aplicó decimation, compresión de amplitud de raíz cúbica y Transformada Discreta de Coseno a las salidas de GCFB para generar coeficientes AFE.
  • Se utilizó el Hidden Markov Model Toolkit para evaluar el rendimiento de AFE en la Saarbruecken Voice Database (SVD) y el conjunto de datos MEEI.

Principales resultados:

  • El enfoque AFE logró una precisión equilibrada del 99,75 % en la clasificación binaria y del 94,38 % en la clasificación multiclase en el conjunto de datos SVD.
  • AFE superó significativamente a HFCC (95,6 %), FDLP (94,8 %) y MFCC (93,85 %) en la clasificación binaria en SVD.
  • AFE demostró una precisión superior en la clasificación multiclase (94,38 %) en comparación con HFCC (72,93 %), FDLP (69,66 %) y MFCC (60,03 %) en SVD.
  • Se logró una precisión equilibrada del 100 % en la base de datos MEEI para la clasificación de voz patológica.

Conclusiones:

  • El enfoque AFE propuesto proporciona un conjunto de características altamente discriminatorio para la clasificación de patologías de la voz.
  • El rendimiento de AFE sugiere potencial para mejorar el cribado clínico y el diagnóstico de trastornos de la voz.
  • La extracción de características basada en Gammachirp FilterBank ofrece una dirección prometedora para sistemas PVR avanzados.