Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Speaker verification

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    Feature selection and model optimization for semi-supervised speaker spotting
    (European Signal Processing Conference, EUSIPCO, 2016) Chetupalli, S.R.; Gopalakrishnan, A.; Sreenivas, T.V.
    We explore, experimentally, feature selection and optimization of stochastic model parameters for the problem of speaker spotting. Based on an initially identified segment of speech of a speaker, an iterative model refinement method is developed along with a latent variable mixture model so that segments of the same speaker are identified in a long speech record. It is found that a GMM with moderate number of mixtures is better suited for the task than a large number mixture model as used in speaker identification. Similarly, a PCA based low-dimensional projection of MFCC based feature vector provides better performance. We show that about 6 seconds of initially identified speaker data is sufficient to achieve > 90% performance of speaker segment identification. © 2016 IEEE.
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    Factor analysis methods for joint speaker verification and spoof detection
    (Institute of Electrical and Electronics Engineers Inc., 2017) Dhanush, B.K.; Suparna, S.; Aarthy, R.; Likhita, C.; Shashank, D.; Harish, H.; Ganapathy, S.
    The performance of a speaker verification system is severely degraded by spoofing attacks generated from artificial speech synthesizers. Recently, several approaches have been proposed for classifying natural and synthetic speech (spoof detection) which can be used in conjunction with a speaker verification system. In this paper, we attempt to develop a joint modelling approach which can detect the presence of spoofing attacks while also performing the speaker verification task. We propose a factor modelling approach where the spoof variability subspace and the speaker variability subspace are jointly trained. The lower dimensional projections in these subspaces are used for speaker verification as well as spoof detection tasks. We also investigate the benefits of linear discriminant analysis (LDA), widely used in speaker recognition, for the spoof detection task. Several experiments are performed using the speaker and spoofing (SAS) database. For speaker verification, we compare the performance of the proposed method with a baseline method of fusing a conventional speaker verification system and a spoof detection system. In these experiments, the proposed approach provides substantial improvements for spoof detection (relative improvements of 20% in EER over the baseline) as well as speaker verification under spoofing conditions (relative improvements of 40% in EER over the baseline). © 2017 IEEE.