ISCA Archive ICSLP 1994
ISCA Archive ICSLP 1994

A new probabilistic framework for connectionist time alignment

Patrick Haffner

To build optimally effective word classifiers, one research direction in speech recognition is to train a connectionist architecture with a gradient back-propagation procedure that minimises the word error rate directly. The first step was the integration of the DTW alignment procedure into the architecture: the Multi-State Time Delay Neural Network (MS-TDNN[6]) architecture was successfully demonstrated on several large speech recognition tasks. In this paper, we provide an HMM probabilistic framework for the alignment procedure, with improved experimental results. Moreover, applying a unified HMM/connectionist formalation to global speech recognition systems suggests ways to exchange expertise between both fields.


Cite as: Haffner, P. (1994) A new probabilistic framework for connectionist time alignment. Proc. 3rd International Conference on Spoken Language Processing (ICSLP 1994), 1559-1562

@inproceedings{haffner94_icslp,
  author={Patrick Haffner},
  title={{A new probabilistic framework for connectionist time alignment}},
  year=1994,
  booktitle={Proc. 3rd International Conference on Spoken Language Processing (ICSLP 1994)},
  pages={1559--1562}
}