Publication detail

Out-of-Vocabulary Word Recovery Using FST-Based Subword Unit Clustering in a Hybrid ASR System

EGOROVA, E. BURGET, L.

Original Title

Out-of-Vocabulary Word Recovery Using FST-Based Subword Unit Clustering in a Hybrid ASR System

Type

conference paper

Language

English

Original Abstract

The paper presents a new approach to extracting useful information from out-of-vocabulary (OOV) speech regions in ASR system output. The system makes use of a hybrid decoding network with both words and sub-word units. In the decoded lattices, candidates for OOV regions are identified as sub-graphs of sub-word units. To facilitate OOV word recovery, we search for recurring OOVs by clustering the detected candidate OOVs. The metrics for clustering is based on a comparison of the sub-graphs corresponding to the OOV candidates. The proposed method discovers repeating outof- vocabulary words and finds their graphemic representation more robustly than more conventional techniques taking into account only one best sub-word string hypotheses.

Keywords

Out-of-vocabulary Words, Robust ASR

Authors

EGOROVA, E.; BURGET, L.

Released

15. 4. 2018

Publisher

IEEE Signal Processing Society

Location

Calgary

ISBN

978-1-5386-4658-8

Book

Proceedings of ICASSP 2018

Pages from

5919

Pages to

5923

Pages count

5

URL

https://www.fit.vut.cz/research/publication/11725/

BibTex

@inproceedings{BUT155047,
  author="Ekaterina {Egorova} and Lukáš {Burget}",
  title="Out-of-Vocabulary Word Recovery Using FST-Based Subword Unit Clustering in a Hybrid ASR System",
  booktitle="Proceedings of ICASSP 2018",
  year="2018",
  pages="5919--5923",
  publisher="IEEE Signal Processing Society",
  address="Calgary",
  doi="10.1109/ICASSP.2018.8462221",
  isbn="978-1-5386-4658-8",
  url="https://www.fit.vut.cz/research/publication/11725/"
}