Speech Recognition Experiments with Perceptrons
Abstract
Artificial neural networks (ANNs) are capable of accurate recognition of simple speech vocabularies such as isolated digits [1]. This paper looks at two more difficult vocabularies, the alphabetic E-set and a set of polysyllabic words. The E-set is difficult because it contains weak discriminants and polysyllables are difficult because of timing variation. Polysyllabic word recognition is aided by a time pre-alignment technique based on dynamic pro(cid:173) gramming and E-set recognition is improved by focusing attention. Recogni(cid:173) tion accuracies are better than 98% for both vocabularies when implemented with a single layer perceptron.
Cite
Text
Burr. "Speech Recognition Experiments with Perceptrons." Neural Information Processing Systems, 1987.Markdown
[Burr. "Speech Recognition Experiments with Perceptrons." Neural Information Processing Systems, 1987.](https://mlanthology.org/neurips/1987/burr1987neurips-speech/)BibTeX
@inproceedings{burr1987neurips-speech,
title = {{Speech Recognition Experiments with Perceptrons}},
author = {Burr, David J.},
booktitle = {Neural Information Processing Systems},
year = {1987},
pages = {144-153},
url = {https://mlanthology.org/neurips/1987/burr1987neurips-speech/}
}