Cubic Law and MAP Compensation Techniques for Robust Text-Independent Speaker Identification

Harry Anacleto, David Chavez

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

Automatic speaker recognition is drastically degraded in presence of noise. This paper focuses on the application of the cubic law and histogram mapping for the text-independent speaker recognition task. Our aim of this study is the application of these two methods in the feature extraction stage of the Power-Normalized Cepstral Coefficients (PNCC) and the conventional Mel Frequency Cepstral Coefficients (MFCC) techniques. Recognition results show that the cubic law combined with the histogram mapping improve the recognition rates.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 2020 International Conference on Systems, Signals and Image Processing, IWSSIP 2020
EditoresAnselmo C. Paiva, Aura Conci, Geraldo Braz, Joao Dallyson S. Almeida, Leandro A. F. Fernandes
EditorialIEEE Computer Society
Páginas387-392
Número de páginas6
ISBN (versión digital)9781728175393
DOI
EstadoPublicada - jul. 2020
Evento27th International Conference on Systems, Signals and Image Processing, IWSSIP 2020 - Niteroi, Brasil
Duración: 1 jul. 20203 jul. 2020

Serie de la publicación

NombreInternational Conference on Systems, Signals, and Image Processing
Volumen2020-July
ISSN (versión impresa)2157-8672
ISSN (versión digital)2157-8702

Conferencia

Conferencia27th International Conference on Systems, Signals and Image Processing, IWSSIP 2020
País/TerritorioBrasil
CiudadNiteroi
Período1/07/203/07/20

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