Bibtex entry:

@inproceedings{EUSIPCO:Fel96,
  key  ={EUSIPCO:Fel96},
  title={Extraction of LP-Based Features from One-Bit Quantized Speech Signals f
or Recognition Purposes},
  author={M. Felici and A. Ferrari and M. Borgatti and R. Guerrieri},
  booktitle={Proc. of EUSIPCO-96 Trieste-Italy},
  month=sep,
  pages={121-124},
  year={1996},
  keywords={speech recognition isolated word linear predictive zero crossing},
  abstract={
A simplified fixed-point computation of cepstral coefficients, based
 on linear predictive analysis and infinite clipping of speech
 signals, is described.  The autocorrelation function of the clipped
 signal is directly used to compute the linear predictor coefficients.
 The performance of an isolated word recognition system based on these
 coefficients is presented and compared with a system which uses
 standard linear predictive cepstral features.  The results show that
 these coefficients can be efficiently used for small dictionary
 speech recognition systems and, since the analog-to-digital
 conversion can be avoided, they are suitable for a low-voltage and
 low-power hardware implementation.}, 
}

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