Abstract
Feature extraction has the ability to improve the performance of speaker identification systems. This paper studies the significance of low-variance multitaper Mel-frequency cepstral coefficient (multitaper MFCC) features for Multilingual speaker identification with the constraint of limited data. The speaker identification study is conducted using 30 speakers of our own database. Sine-weighted cepstrum estimator (SWCE) taper MFCC features are extracted and modeled using Gaussian Mixture Model (GMM)-Universal Background Model (UBM). The results show that the multitaper MFCC approach performs better than the conventional Hamming window MFCC technique in all the speaker identification experiments.
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Nagaraja, B.G., Jayanna, H.S. (2012). Multilingual Speaker Identification with the Constraint of Limited Data Using Multitaper MFCC. In: Thampi, S.M., Zomaya, A.Y., Strufe, T., Alcaraz Calero, J.M., Thomas, T. (eds) Recent Trends in Computer Networks and Distributed Systems Security. SNDS 2012. Communications in Computer and Information Science, vol 335. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34135-9_13
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DOI: https://doi.org/10.1007/978-3-642-34135-9_13
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