Speech recognition systems for irregularly-spelled languages like English normally require hand-written pronunciations. In this paper, we describe a system for automatically obtaining pronunciations of words for which pronunciations are not available, but for which transcribed data exists. Our method integrates information from the letter sequence and from the acoustic evidence. The novel aspect of the problem that we address is the problem of how to prune entries from such a lexicon (since, empirically, lexicons with too many entries do not tend to be good for ASR performance). Experiments on various ASR tasks show that, with the proposed framework, starting with an initial lexicon of several thousand words, we are able to learn a lexicon which performs close to a full expert lexicon in terms of WER performance on test data, and is better than lexicons built using G2P alone or with a pruning criterion based on pronunciation probability.
Cite as: Zhang, X., Manohar, V., Povey, D., Khudanpur, S. (2017) Acoustic Data-Driven Lexicon Learning Based on a Greedy Pronunciation Selection Framework. Proc. Interspeech 2017, 2541-2545, doi: 10.21437/Interspeech.2017-588
@inproceedings{zhang17h_interspeech,
author={Xiaohui Zhang and Vimal Manohar and Daniel Povey and Sanjeev Khudanpur},
title={{Acoustic Data-Driven Lexicon Learning Based on a Greedy Pronunciation Selection Framework}},
year=2017,
booktitle={Proc. Interspeech 2017},
pages={2541--2545},
doi={10.21437/Interspeech.2017-588}
}