An Extensible Speaker Identification SIDEKIT in Python

Abstract : SIDEKIT is a new open-source Python toolkit that includes a large panel of state-of-the-art components and allow a rapid prototyping of an end-to-end speaker recognition system. For each step from front-end feature extraction, normalization, speech activity detection, modelling, scoring and visualization, SIDEKIT offers a wide range of standard algorithms and flexible interfaces. The use of a single efficient programming and scripting language (Python in this case), and the limited dependencies, facilitate the deployment for industrial applications and extension to include new algorithms as part of the whole tool-chain provided by SIDEKIT. Performance of SIDEKIT is demonstrated on two standard evaluation tasks, namely the RSR2015 and NIST-SRE 2010.
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Communication dans un congrès
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016), 2016, Shanghai, China. pp.5095-5099, 2016, Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. 〈10.1109/ICASSP.2016.7472648〉
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https://hal.archives-ouvertes.fr/hal-01433157
Contributeur : Sylvain Meignier <>
Soumis le : vendredi 24 mars 2017 - 00:01:48
Dernière modification le : mardi 19 juin 2018 - 11:50:04
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Anthony Larcher, Kong Aik Lee, Sylvain Meignier. An Extensible Speaker Identification SIDEKIT in Python. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016), 2016, Shanghai, China. pp.5095-5099, 2016, Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. 〈10.1109/ICASSP.2016.7472648〉. 〈hal-01433157〉

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