Segmentation of Audio Visual Malay Digit Utterances Using Endpoint Detection

  • Mohd Ridzuwary Mohd Zainal
  • Aini Hussain
  • Salina Abdul Samad
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 156)

Abstract

An endpoint detection algorithm is utilised for segmentation of audio video Malay utterances. An audio visual Malay speech database of subjects uttering numerical digits is used. Synchronization between video frames and audio signals is taken into considerations for audio visual speech processing. The proposed system is able to group together the individual syllables that make up each of the uttered Malay digits.

Keywords

Speech Recognition Video Frame Video Stream Audio Signal Automatic Speech Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Potamianos, G., Neti, C., Luettin, J., Matthews, I.: Audio-visual automatic speech recognition: An overview. Issues in Visual and Audio-visual Speech Process (2004)Google Scholar
  2. 2.
    Petrou, M., Kittler, J.: Optimal edge detectors for ramp edges. IEEE Trans. Pattern Anal. Mach. Intell. 13, 483–491 (1991)CrossRefGoogle Scholar
  3. 3.
    Seman, N., Jusoff, K.: Acoustic pronunciation variations modeling for standard Malay speech recognition. Computer and Information Sci. J. 1(4), 112–120 (2008)Google Scholar
  4. 4.
    Seman, N., Jusoff, K.: Automatic segmentation and labeling for spontaneous standard Malay speech recognition. In: Int. Conf. Adv. Comput. Theory and Engineering, pp. 59–63 (2008)Google Scholar
  5. 5.
    Li, Q., Zheng, J., Tsai, A., Zhou, Q.: Robust endpoint detection and energy normalization for real-time speech and speaker recognition. IEEE Trans. Speech Audio Process. 10, 146–157 (2002)CrossRefGoogle Scholar
  6. 6.
    Sabah, R., Ainon, R.N.: Isolated digit speech recognition in Malay language using neuro-fuzzy approach. In: 3rd Asia Int. Conf. Modelling and Simulation, pp. 336–340 (2009)Google Scholar
  7. 7.
    Al-Haddad, S.A.R., Samad, S.A., Hussain, A.: Automatic Recognition for Malay Isolated Digits. In: 3rd Int. Colloq. Signal Process. and Its Appl., Melaka, Malaysia, March 9-11 (2007)Google Scholar
  8. 8.
    Al-Haddad, S.A.R., Samad, S.A., Hussain, A., Ishak, K.A.: Isolated Malay digit recognition using pattern recognition fusion of dynamic time warping and hidden markov models. Am. J. of Appl. Sci. 5(6), 714–720 (2008), doi:10.3844/ajassp.2008.714.720.CrossRefGoogle Scholar
  9. 9.
    Al-Haddad, S.A.R., Samad, S.A., Hussain, A., Ishak, K.A., Noor, A.O.A.: Robust speech recognition using fusion techniques and adaptive filtering. American Journal of Applied Sciences 6(2), 290–295 (2009), doi:10.3844/ajassp.2009.290.295Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  • Mohd Ridzuwary Mohd Zainal
    • 1
  • Aini Hussain
    • 1
  • Salina Abdul Samad
    • 1
  1. 1.Universiti Kebangsaan MalaysiaBangiMalaysia

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