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

Anacleto H.; Chavez D. · International Conference on Systems, Signals, and Image Processing (2020) · DOI: 10.1109/iwssip48289.2020.9145319

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.