Robust isolated speech recognition using binary masks

@article{Karadogan2010RobustIS,
  title={Robust isolated speech recognition using binary masks},
  author={Seliz G. Karadogan and Jan Larsen and Michael Syskind Pedersen and Jesper B{\"u}nsow Boldt},
  journal={2010 18th European Signal Processing Conference},
  year={2010},
  pages={1988-1992}
}
In this paper, we represent a new approach for robust speaker independent ASR using binary masks as feature vectors. This method is evaluated on an isolated digit database, TIDIGIT in three noisy environments (car, bottle and cafe noise types taken from the DRCD Sound Effects Library). Discrete Hidden Markov Models are used for the recognition and the observation vectors are quantized with the K-means algorithm using a Hamming distance. It is found that a recognition rate as high as 92% for… CONTINUE READING

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Key Quantitative Results

  • It is found that a recognition rate as high as 92% for clean speech is achievable using Ideal Binary Masks (IBM) where we assume prior target and noise information is available.

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