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Updated: Jan 31, 2026

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Un marco híbrido de CNN y aprendizaje por refuerzo para la identificación del hablante utilizando características de

Fereshteh Manafzadeh Heir1, Hossein Najafzadeh2, Sarvenaz Erfani3

  • 1Department of Computer Engineering, Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran.

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|January 29, 2026
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Resumen

Este estudio presenta un modelo híbrido de aprendizaje profundo para la identificación del hablante, superando a los métodos tradicionales. Los Mel-espectrogramas con mecanismos de atención ofrecen una extracción superior de características vocales para una autenticación biométrica robusta.

Palabras clave:
Análisis de características acústicasAutenticación biométricaTransformada continua de waveletRedes neuronales convolucionalesAprendizaje profundo por refuerzoMel-espectrogramaIdentificación del hablante

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Área de la Ciencia:

  • Biometría y Seguridad
  • Inteligencia Artificial y Aprendizaje Automático
  • Procesamiento de Señales

Sus antecedentes:

  • La identificación del hablante es crucial para la autenticación biométrica.
  • Se necesita una extracción de características robusta para capturar características vocales únicas.
  • Los métodos actuales requieren arquitecturas avanzadas de aprendizaje profundo.

Objetivo del estudio:

  • Desarrollar una novedosa arquitectura híbrida de aprendizaje profundo para la identificación del hablante consciente de la confianza.
  • Comparar Mel-espectrogramas con autoatención frente a la transformada continua de wavelet para la extracción de características.
  • Evaluar el impacto de la integración del aprendizaje por refuerzo (RL) en las redes neuronales convolucionales (CNN).

Principales métodos:

  • Se desarrolló una arquitectura híbrida CNN-RL para la identificación del hablante.
  • Se emplearon dos métodos de extracción de características: Mel-espectrogramas (Método 1) y transformada continua de wavelet (Método 2).
  • Se utilizó el conjunto de datos LibriSpeech con validación cruzada de 5 pliegues y ANOVA evaluó el poder discriminatorio de las características.

Principales resultados:

  • El Mel-espectrograma con atención (Método 1) logró una precisión del 87,60% y un ROC-AUC del 99,54%.
  • La transformada continua de wavelet (Método 2) logró una precisión del 77,60% y un ROC-AUC del 98,21%.
  • La integración de RL mejoró significativamente los valores de referencia solo de CNN (p < 0,05) y redujo la variabilidad del rendimiento.

Conclusiones:

  • Las representaciones a escala Mel con mecanismos de atención proporcionan una capacidad discriminatoria superior para la identificación del hablante.
  • Las arquitecturas híbridas CNN-RL mejoran la robustez y reducen la incertidumbre en la autenticación biométrica.
  • Los métodos propuestos ofrecen un avance significativo en los sistemas de identificación del hablante conscientes de la confianza.