The Journal of the Acoustical Society of Korea

The Acoustical Society of Korea (한국음향학회)
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Performance Improvement of Microphone Array Speech Recognition Using Features Weighted Mahalanobis Distance

가중특징 Mahalanobis거리를 이용한 마이크 어레이 음석인식의 성능향상

  • Received : 2009.09.14
  • Accepted : 2009.11.12
  • Published : 2010.03.31
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Abstract

In this paper, we present the use of the Features Weighted Mahalanobis Distance (FWMD) in improving the performance of Likelihood Maximizing Beamforming (Limabeam) algorithm in speech recognition for microphone array. The proposed approach is based on the replacement of the traditional distance measure in a Gaussian classifier with adding weight for different features in the Mahalanobis distance according to their distances after the variance normalization. By using Features Weighted Mahalanobis Distance for Limabeam algorithm (FWMD-Limabeam), we obtained correct word recognition rate of 90.26% for calibrate Limabeam and 87.23% for unsupervised Limabeam, resulting in a higher rate of 3% and 6% respectively than those produced by the original Limabearn. By implementing a HM-Net speech recognition strategy alternatively, we could save memory and reduce computation complexity.

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References

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