{"id":167023,"date":"2014-06-01T00:00:00","date_gmt":"2014-06-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/a-latent-dirichlet-allocation-based-front-end-for-speaker-verification\/"},"modified":"2018-10-16T21:29:29","modified_gmt":"2018-10-17T04:29:29","slug":"a-latent-dirichlet-allocation-based-front-end-for-speaker-verification","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-latent-dirichlet-allocation-based-front-end-for-speaker-verification\/","title":{"rendered":"A Latent Dirichlet Allocation Based Front-End for Speaker Verification"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Latent Dirichlet Allocation is a powerful topic model used heavily in natural language processing, image processing and biomedical signal processing fields to discover hidden structures behind observed data. In this work, we have adopted a variant of LDA for continuous descriptor vectors and use this model as a front-end for speaker verification similar to popular i-vector front-end. We have proposed an efficient hierarchical acoustic vocabulary creation method and presented a speaker verification system using latent topic probability features obtained using LDA front-end. We analysed the performance of the LDA front-end for various vocabulary and topic sizes, and obtained encouraging results on NIST SRE corpora. The proposed system is shown to improve the performance of an i-vector-PLDA baseline system when tested on NIST SRE12 corpora.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Latent Dirichlet Allocation is a powerful topic model used heavily in natural language processing, image processing and biomedical signal processing fields to discover hidden structures behind observed data. 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