A text-independent speaker verification model: A comparative analysis
This work addresses the problem of speaker recognition for voice biometrics applications, but it is incremental as it compares existing methods without introducing new ones.
The paper tackles the challenge of selecting efficient techniques for text-independent speaker verification by comparing various feature extraction, dimensionality reduction, and classification methods, finding that MFCC with SVM yields the best results, though specific numbers are not provided.
The most pressing challenge in the field of voice biometrics is selecting the most efficient technique of speaker recognition. Every individual's voice is peculiar, factors like physical differences in vocal organs, accent and pronunciation contributes to the problem's complexity. In this paper, we explore the various methods available in each block in the process of speaker recognition with the objective to identify best of techniques that could be used to get precise results. We study the results on text independent corpora. We use MFCC (Melfrequency cepstral coefficient), LPCC (linear predictive cepstral coefficient) and PLP (perceptual linear prediction) algorithms for feature extraction, PCA (Principal Component Analysis) and tSNE for dimensionality reduction and SVM (Support Vector Machine), feed forward, nearest neighbor and decision tree algorithms for classification block in speaker recognition system and comparatively analyze each block to determine the best technique