ISCA Archive ICSLP 1994
ISCA Archive ICSLP 1994

Connected digit recognition using connectionist probability estimators and mixture-Gaussian densities

David M. Lubensky, Ayman O. Asadi, Jayant M. Naik

We report on some recent improvements to a continuous density hidden Markov model (CDHMM) based speech recognition system which is being developed for a variety of telecommunication applications. In particular, we are concerned with computing the emission probabilities of an HMM state using a combination of multi-layer perceptrons (MLPs) as probability estimators and mixture-Gaussian densities. Using MLPs as state observation estimators has been shown to improve accuracy on a speaker verification task [11]. In this paper, we describe a connected digit recognition system which incorporates both MLPs and mixture, Gaussian densities. The results are reported on the standard Texas Instruments (TI) connected digit database [9] which was digitally filtered to the telephone bandwidth (300 Hz-3.2 kHz) and downsampled to 8 kHz. A hybrid MLP/HMM system led to 15% improvement in performance. The final string error rate is 1.7% for unknown length strings.


Cite as: Lubensky, D.M., Asadi, A.O., Naik, J.M. (1994) Connected digit recognition using connectionist probability estimators and mixture-Gaussian densities. Proc. 3rd International Conference on Spoken Language Processing (ICSLP 1994), 295-298

@inproceedings{lubensky94_icslp,
  author={David M. Lubensky and Ayman O. Asadi and Jayant M. Naik},
  title={{Connected digit recognition using connectionist probability estimators and mixture-Gaussian densities}},
  year=1994,
  booktitle={Proc. 3rd International Conference on Spoken Language Processing (ICSLP 1994)},
  pages={295--298}
}