Abstract
This paper compares performances between GMM-UBM classifier and SVM classifier with GMM supervector as the linear kernel for text-independent speaker verification. The MFCC feature set has been used for this comparison. Experimental evaluation was conducted on the POLYCOST database. The importance of utterance partitioning for training speech has been discussed. Results reveal that, without utterance partitioning, the accuracy of SVM classifier with GMM supervectors for small test segment is poor. For proper utterance partitioning of the training speech, the SVM classifier with GMM supervectors performs significantly better compared to GMM-UBM baseline. The detailed derivation of GMM supervector has also been discussed.
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Sen, N., Patil, H.A., Mandal, S.K.D., Rao, K.S. (2013). Importance of Utterance Partitioning in SVM Classifier with GMM Supervectors for Text-Independent Speaker Verification. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_76
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DOI: https://doi.org/10.1007/978-3-319-03844-5_76
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03843-8
Online ISBN: 978-3-319-03844-5
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