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A Feature Level Fusion Scheme for Robust Speaker Identification

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Big Data, Cloud and Applications (BDCA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 872))

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Abstract

For speaker identification purposes, features are first extracted and then compared with those of the training set to find the closest match. So, finding effective and robust features for classifying speakers is beneficial to improve the overall identification performance, especially in the presence of noise. In this paper, a new method of feature extraction based on feature fusion is proposed, where Gammatone Frequency Cepstral Coefficients (GFCC) and wavelet components are extracted and fused for training and testing the Support Vector Machines (SVM) classifier. The performance of the proposed scheme is validated and compared with conventional GFCC using clean and noise corrupted signals from Voxforge database. From the experimental results, it is evident that our algorithm has a higher identification accuracy compared to baseline GFCC.

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Author information

Authors and Affiliations

  1. Team Networks, Telecoms & Multimedia, LIM@II-FSTM, B.P. 146, 20650, Mohammedia, Morocco

    Sara Sekkate, Mohammed Khalil & Abdellah Adib

Authors
  1. Sara Sekkate
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  2. Mohammed Khalil
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  3. Abdellah Adib
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Corresponding author

Correspondence to Sara Sekkate .

Editor information

Editors and Affiliations

  1. Abdelmalek Essaâdi University, Tétouan, Morocco

    Youness Tabii

  2. Abdelmalek Essaâdi University, Tétouan, Morocco

    Mohamed Lazaar

  3. Abdelmalek Essaâdi University, Tétouan, Morocco

    Mohammed Al Achhab

  4. Université Ibn-Tofail, Tétouan, Morocco

    Nourddine Enneya

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Sekkate, S., Khalil, M., Adib, A. (2018). A Feature Level Fusion Scheme for Robust Speaker Identification. In: Tabii, Y., Lazaar, M., Al Achhab, M., Enneya, N. (eds) Big Data, Cloud and Applications. BDCA 2018. Communications in Computer and Information Science, vol 872. Springer, Cham. https://doi.org/10.1007/978-3-319-96292-4_23

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  • DOI: https://doi.org/10.1007/978-3-319-96292-4_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96291-7

  • Online ISBN: 978-3-319-96292-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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