A Gaussian Selection Method for Speaker Verification with Short Utterances

Método de selección de gaussianas para la verificación de locutores con señales cortas

Flavio J. Reyes Díaz, Gabriel Hernández Sierra, and José Calvo de Lara

Advanced Technologies Application Center (CENATAV), La Havana, Cuba. freyes@cenatav.co.cu, gsierra@cenatav.co.cu, jcalvo@cenatav.co.cu

Abstract

Speaker recognition systems frequently use GMM-MAP method for modeling speakers. This method represents the speaker using a Gaussian mixture. However, in this mixture not all Gaussian components are truly representative of the speaker. In order to remove the model redundancy, this work proposes a Gaussian selection method to achieve a new GMM model only with the more representative Gaussian components. The results of speaker verification experiments applying the proposal show a similar performance to the baseline; however, the speaker models used have a reduction of 80% compared to the speaker model used as the baseline. Our proposal was also applied to speaker recognition system with short test signals of 15, 5 and 3 seconds obtaining an improvement in EER of 0.43%, 2.64% and 1.60%, respectively, compared to the baseline. The application of this method in real or embedded speaker verification systems could be very useful for reducing computational and memory cost.

Keywords: Speaker verification, Gaussian components selection, cumulative vector, short utterance.

Resumen

Los sistemas de reconocimiento de locutores con frecuencia utilizan el método GMM-MAP para modelar locutores. Sin embargo, en estos modelos no todas las componentes gaussianas son representativas del locutor. Con el fin de eliminar dicha redundancia, proponemos un método de selección de gaussianas obteniendo un nuevo modelo con las componentes gaussianas más representativas. Los resultados experimentales muestran un rendimiento similar a la línea de base, no obstante los modelos obtenidos presentan una reducción del 80% respecto al modelo del locutor utilizado en la línea base. Los métodos propuestos son aplicados sobre señales de prueba más cortas, 15, 5 y 3 segundos; mejorando el EER de 0,43%, 2,64% y 1,60% respectivamente en comparación con la línea base. La aplicación del método propuesto en sistemas reales de verificación podría ser muy útil para reducir el costo computacional y la carga en memoria.

Palabras clave: Verificación de locutores, selección de componentes gaussianas, vector acumulativo, señales cortas.

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