Unimodal late fusion for NIST i-vector challenge on speaker detection

Ali, H., D'Avila Garcez, A.S., Tran, S.N., Zhou, X. & Iqbal, K. (2014). Unimodal late fusion for NIST i-vector challenge on speaker detection. Electronics Letters, 50(15), pp. 1098-1100. doi: 10.1049/el.2014.1207

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Abstract

Speaker detection is a very interesting machine learning task for which the latest i-vector challenge has been coordinated by the National Institute of Standards and Technology (NIST). A simple late fusion approach for the speaker detection task on the i-vector challenge is presented. The approach is based on the late fusion of scores from the cosine distance method (the baseline) and the scores obtained from linear discriminant analysis. The results show that by adapting the simple late fusion approach, the framework can outperform the baseline score for the decision cost function on the NIST i-vector machine learning challenge.

Item Type: Article
Additional Information: (c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Informatics > Department of Computing
URI: http://openaccess.city.ac.uk/id/eprint/14270

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