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

Harry Anacleto; David Chavez

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

Harry Anacleto, David Chavez

Pontificia Universidad Catolica del Peru

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión 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 original	Español
Título de la publicación alojada	International Conference on Systems, Signals, and Image Processing
Páginas	387-392
Número de páginas	6
Volumen	2020-July
Estado	Publicada - 1 jul. 2020
Publicado de forma externa	Sí

Citar esto

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title = "Cubic Law and MAP Compensation Techniques for Robust Text-Independent Speaker Identification",

abstract = "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.",

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AU - Anacleto, Harry

AU - Chavez, David

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N2 - 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.

AB - 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.

M3 - Contribución a la conferencia

VL - 2020-July

SP - 387

EP - 392

BT - International Conference on Systems, Signals, and Image Processing

ER -