Horndasch A, Kaufhold C, Nöth E (2016)
Publication Language: English
Publication Status: Published
Publication Type: Book chapter / Article in edited volumes
Publication year: 2016
Publisher: Springer
Edited Volumes: Text, Speech, and Dialogue. TSD 2016
Series: Lecture Notes in Computer Science
City/Town: Cham
Book Volume: 9924
Pages Range: 486-494
ISBN: 978-3-319-45509-9
URI: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2016/Horndasch16-HTA.pdf
DOI: 10.1007/978-3-319-45510-5_56
The paper explains and illustrates how the concept of word classes can be added to the widely used open-source speech recognition toolkit Kaldi. The suggested extensions to existing Kaldi recipes are limited to the word-level grammar (G) and the pronunciation lexicon (L) models. The implementation to modify the weighted finite state transducers employed in Kaldi makes use of the OpenFST library. In experiments on small and mid-sized corpora with vocabulary sizes of 1.5 K and 5.5 K respectively a slight improvement of the word error rate is observed when the approach is tested with (hand-crafted) word classes. Furthermore it is shown that the introduction of sub-word unit models for open word classes can help to robustly detect and classify out-of-vocabulary words without impairing word recognition accuracy.
APA:
Horndasch, A., Kaufhold, C., & Nöth, E. (2016). How to add word classes to the Kaldi speech recognition toolkit. In Sojka P., Horák A., Kopeček I., Pala K. (Eds.), Text, Speech, and Dialogue. TSD 2016. (pp. 486-494). Cham: Springer.
MLA:
Horndasch, Axel, Caroline Kaufhold, and Elmar Nöth. "How to add word classes to the Kaldi speech recognition toolkit." Text, Speech, and Dialogue. TSD 2016. Ed. Sojka P., Horák A., Kopeček I., Pala K., Cham: Springer, 2016. 486-494.
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