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Audio-visual speaker identification with asynchronous articulatory feature

Audio-visual speaker identification with asynchronous articulatory feature

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  • Author(s): Yanxiang Chen 1  and  M. Liu 2
    • View affiliations
    • Affiliations: 1: School of Computer Science & Information, Hefei University of Technology, Hefei, People's Republic of China
      2: Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, USA
  • Source: Volume 46, Issue 3, 4 February 2010, p. 242 – 243
    DOI:  10.1049/el.2010.3206 , Print ISSN 0013-5194, Online ISSN 1350-911X
© The Institution of Engineering and Technology
Published

A visual component is considerably supplementary to audio information and the apparent asynchrony between acoustic and visual cues may be effectively represented by an asynchronous articulatory feature. A new approach to speaker identification using an articulatory feature-based audio-visual model based on the dynamic Bayesian network is presented. Considerably satisfactory results were achieved in experiments on the audio-visual bimodal CMU database.

Inspec keywords: audio-visual systems; speaker recognition; belief networks

Other keywords: articulatory feature-based audio-visual model; audio-visual speaker identification; Bayesian network; visual component; apparent asynchrony; audio-visual bimodal CMU database; audio information; asynchronous articulatory feature

Subjects: Speech recognition and synthesis; Knowledge engineering techniques; Speech processing techniques

References

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      • Livescu, K., Cetin, O., Hasegawa-Johnson, M., King, S., Bartels, C., Borges, N., Kantor, A., Lal, P., Yung, L., Bezman, A., Dawson-Haggerty, S., Woods, B.: `Articulatory feature-based methods for acoustic and audio-visual speech recognition: 2006 JHU Summer Workshop Final Report', Tech. Rep. WS06, 2006.
    2. 2)
      • Zhang, Y., Diao, Q., Huang, S., Hu, W., Bartels, C., Bilmes, J.: `DBN based multi-stream models for speech', Proc. of Int. Conf. on Acoustic, Speech and Signal Processing, (ICASSP), 2003, Hong Kong, China, p. 836–839.
    3. 3)
      • C.P. Browman , L. Goldstein . Articulatory phonology: an overview. Phonetica , 155 - 180
    4. 4)
      • Chu, S.M., Huang, T.S.: `Multi-model sensory fusion with application to audio-visual speech recognition', Proc. European Conf. on Speech Communication and Technology, (Eurospeech), 2001, 2001, Aalborg, Denmark.
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      • Bilmes, J., Zweig, G.: `The graphical models toolkit: an open source software system for speech and time-series processing', Proc. of Int. Conf. on Acoustic, Speech and Signal Processing, (ICASSP), 2002, Florida, USA, p. 3916–3919.
    6. 6)
      • T. Chen . Audiovisual speech processing. IEEE Trans. Signal Process. , 1 , 9 - 21
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