Comparison of Techniques with Speech Enhancement and Nonlinear Rectification for Robust Speaker Identificatio

Harry Anacleto Silva

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

Resumen

Automatic speaker recognition is about the identification of a person based on his or her characteristic voice with the help of machines. However, this identification is drastically degraded due to adverse conditions. Our aim of this study is to evaluate the performance of the techniques Power- Normalized Cepstral Coefficients (PNCC) and Mel Frequency Cepstral Coefficients (MFCC) on NIST 2002 database, as well as using Wavelet Denoising and Cubic Law as techniques to speech enhancement and nonlinear rectification to improve speaker recognition rates. Results showed that combined Wavelet Denoising and Cubic Law get improved the recognition rates under noisy conditions.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2022 3rd International Conference on Electronics, Communications and Information Technology, CECIT 2022
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas170-175
Número de páginas6
ISBN (versión digital)9798350331974
DOI
EstadoPublicada - 2022
Evento3rd International Conference on Electronics, Communications and Information Technology, CECIT 2022 - Virtual, Online, China
Duración: 23 dic. 202225 dic. 2022

Serie de la publicación

NombreProceedings - 2022 3rd International Conference on Electronics, Communications and Information Technology, CECIT 2022

Conferencia

Conferencia3rd International Conference on Electronics, Communications and Information Technology, CECIT 2022
País/TerritorioChina
CiudadVirtual, Online
Período23/12/2225/12/22

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