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| Titre: | Feature fusion based on auditory and speech systems for an improved voice biometrics system using artificial neural network |
| Auteur(s): | Cherifi, Youssouf Ismail
Dahimene, Abdelhakim |
| Mots-clés: | Speech Processing
Neural Network
Pattern Recognition
Speaker Recognition
Feature Extraction |
| Date de publication: | 2020 |
| Collection/Numéro: | Proceedings of the 14th IADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing 2020, CGVCVIP 2020 and Proceedings of the 5th IADIS International Conference Big Data Analytics, Data Mining and Computational Intelligence 2020, BigDaCI 2020 and Proceedings of the 9th IADIS International Conference Theory and Practice in Modern Computing 2020, TPMC 2020 - Part of the 14th Multi Conference on Computer Science and Information Systems, MCCSIS 2020;pp. 188-196 |
| Résumé: | In today's world, identifying a speaker has become an essential task. Especially for systems that rely on voice commands or speech in general to operate. These systems use speaker-specific features to identify the individual, features such as Mel Frequency Cepstral Coefficients, Linear Predictive Coding, or Perceptual Linear Predictive. Although these features provide different representations of speech, they can all be considered as either auditory system based (type 1) or speech system based (type 2). In this work, a method of improving existing voice biometrics system is presented. Fusing a type 1 feature with a type 2 feature is evaluated and an artificial neural network is trained and tested on in-campus recorded data set. The results confirm the ability for such an approach to be utilized for improving voice biometrics system, regardless of the underlying task being speaker identification or verification |
| URI/URL: | http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/6588 |
| Collection(s) : | Communications Internationales
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