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Audio-Visual Feature Fusion for Speaker Identification

  • Noor Almaadeed
  • Amar Aggoun
  • Abbes Amira
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7663)

Abstract

Analyses of facial and audio features have been considered separately in conventional speaker identification systems. Herein, we propose a robust algorithm for text-independent speaker identification based on a decision-level and feature-level fusion of facial and audio features. The suggested approach makes use of Mel-frequency Cepstral Coefficients (MFCCs) for audio signal processing, Viola-Jones Haar cascade algorithm for face detection from video, eigenface features (EFF) and Gaussian Mixture Models (GMMs) for feature-level and decision-level fusion of audio and video. Decision-level fusion is carried out using PCA for face and GMM for audio through AND voting. Feature-level fusion is investigated by combining both MFCC (audio) and PCA (face) features to construct a hybrid GMM for each speaker. Testing on GRID, a multi-speaker audio-visual database, shows that the decision-level fusion of PCA (face) and GMM (audio) achieves 98.2 % accuracy and it is almost 15 % more efficient than feature-level fusion.

Keywords

Principal component Analysis Gaussian mixture models speaker identification audio-visual fusion Mel-frequency Cepstral coefficients 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Noor Almaadeed
    • 1
  • Amar Aggoun
    • 1
  • Abbes Amira
    • 2
    • 3
  1. 1.Department of Computer EngineeringBrunel UniversityLondonUK
  2. 2.NIBECUniversity of UlsterJordanstownUK
  3. 3.College of EngineeringQatar UniversityQatar

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