IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
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Improvement of Recognition Performance for Limabeam Algorithm by using MLLR Adaptation

  • Nguyen, Dinh Cuong (Department of information and communication engineering, Yeungnam university) ;
  • Choi, Suk-Nam (Department of information and communication engineering, Yeungnam university) ;
  • Chung, Hyun-Yeol (Department of the information and communication, Yeungnam University)
  • Received : 2013.02.19
  • Accepted : 2013.04.16
  • Published : 2013.08.31
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Abstract

This paper presents a method using Maximum-Likelihood Linear Regression (MLLR) adaptation to improve recognition performance of Limabeam algorithm for speech recognition using microphone array. From our investigation on Limabeam algorithm, we can see that the performance of filtering optimization depends strongly on the supporting optimal state sequence and this sequence is created by using Viterbi algorithm trained with HMM model. So we propose an approach using MLLR adaptation for the recognition of speech uttered in a new environment to obtain better optimal state sequence that support for the filtering parameters' optimal step. Experimental results show that the system embedded with MLLR adaptation presents the word correct recognition rate 2% higher than that of original calibrate Limabeam and also present 7% higher than that of Delay and Sum algorithm. The best recognition accuracy of 89.4% is obtained when we use 4 microphones with 5 utterances for adaptation.

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References

  1. L.J. Griffiths, C.W. Jim, "An alternative approach to linerly constrained adaptive beamforming," IEEE Transaction on Antennas and Propagation, Vol.. AP-30, No. 1, pp.27-34, 1982.
  2. R. Zelinski, "A microphone array with adaptive post-filtering for noise reducetion in reverberant room," Proceeding of International Conference on Acoustics, Speech, and Signal Processing, Vol. 5, pp.2578-2581, 1988.
  3. I.A. McCowan, H. Bourlard, "Microphone array post-filter for diffuse noise filed," Proceeding of International Conference on Acoustics, Speech, and Signal Processing, Vol. 1, pp.905-908, 2002.
  4. S, Leukimmiatis, "An Optimum Microphone Array Post-Filter for Speech Application," Proceeding of International Conference on Spoken Language Processing, Inter-speech, pp.2142-2145, 2006.
  5. M.L. Seltzer, B. Ra, R.M. Stern, "Likelihood - Maziming Beamforming for Robust Hands- Free Speech Recognition," IEEE Transactions on Speech and Audio Processing, Vol. 12, No. 5, pp.489-498, 2004. https://doi.org/10.1109/TSA.2004.832988
  6. M. Wolfel, H.K. Ekenel, "Feature weighted Mahalanobis distance: improved robustness for gauusian classifiers," Proceedings on European Signal Processing conference, 2005.
  7. D.C. Nguyen, H.Y. Chung, "Performance Improvement of Microphone Array Speech Recognition using Feature Weighted Mahalanobis Distance," The Journal of the Acoustical Society of Korea, Vol. 29, No. 1E, pp.1E, 2010.
  8. M.J.F. Gales, P.C. Woodland, "Mean and Variance Adaptation within the MLLR Framework," Cambridge Press, 1996.
  9. A.J. Viterbi, "Error bounds for convolutional codes and an asymptotically optimum decoding algorithm," IEEE Transactions on Information Theory, Vol. 13, No. 2, pp.260-267, 1967. https://doi.org/10.1109/TIT.1967.1054010