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Evaluación automática de la calidad de la voz mediante aprendizaje automático

Yat Chun Au1, Nan Yan2, Manwa L Ng1

  • 1Speech Science Laboratory, Faculty of Education, University of Hong Kong, Hong Kong, China.

Logopedics, phoniatrics, vocology
|February 25, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Los modelos de aprendizaje automático predicen con precisión la gravedad de la disfonía mediante el análisis acústico de la voz. Los algoritmos de gradient boosting, especialmente LightGBM, muestran una concordancia cercana a la de los expertos, mejorando la evaluación clínica objetiva de la voz.

Palabras clave:
DisfoníaGRBASLightGBMLuís Jesusanálisis acústicoaprendizaje automático

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Área de la Ciencia:

  • Patología del Habla y del Lenguaje
  • Lingüística Computacional
  • Ingeniería Biomédica

Sus antecedentes:

  • La evaluación clínica de la voz se basa en calificaciones perceptuales subjetivas de la gravedad de la disfonía.
  • Los métodos existentes carecen de objetividad, reproducibilidad y eficiencia.
  • El análisis acústico automatizado ofrece potencial para la evaluación estandarizada de la voz.

Objetivo del estudio:

  • Desarrollar y validar modelos de aprendizaje automático para la predicción automatizada de la gravedad de la disfonía (parámetro de Calificación de la escala GRBAS).
  • Mejorar la objetividad, reproducibilidad y eficiencia en la evaluación clínica de la voz.
  • Identificar las características acústicas clave predictivas de la gravedad de la disfonía perceptual.

Principales métodos:

  • Se recolectaron 524 muestras de voz sostenida /a/ de tres bases de datos.
  • Se extrajeron 47 características acústicas (espectrales, cepsstrales, de perturbación, basadas en ruido) utilizando Parselmouth (Praat).
  • Se entrenaron y evaluaron cinco clasificadores de aprendizaje automático (DT, RF, XGBoost, LightGBM, CatBoost) utilizando validación cruzada de 5 pliegues.

Principales resultados:

  • Los algoritmos de gradient boosting (LightGBM, CatBoost, XGBoost) superaron a los modelos tradicionales basados en árboles.
  • LightGBM logró el mayor índice kappa ponderado cuadrático (QWK) de 0.945.
  • Las medidas cepsstrales (CPPS, CSID, AVQI) y el HNR fueron los predictores más influyentes de la Calificación, mientras que el jitter y el shimmer contribuyeron mínimamente.

Conclusiones:

  • Los métodos de gradient boosting, en particular LightGBM, demuestran una concordancia cercana a la de los expertos con las calificaciones perceptuales de disfonía.
  • Estos modelos ofrecen herramientas objetivas e interpretables para la evaluación clínica de la voz.
  • La predicción automatizada de la gravedad de la disfonía puede mejorar el flujo de trabajo clínico y la consistencia del diagnóstico.