Publication detail
Text Augmentation for Language Models in High Error Recognition Scenario
BENEŠ, K. BURGET, L.
Original Title
Text Augmentation for Language Models in High Error Recognition Scenario
Type
conference paper
Language
English
Original Abstract
In this paper, we explore several data augmentation strategiesfor training of language models for speech recognition. Wecompare augmentation based on global error statistics withone based on unigram statistics of ASR errors and with labelsmoothingand its sampled variant. Additionally, we investigatethe stability and the predictive power of perplexity estimatedon augmented data. Despite being trivial, augmentation drivenby global substitution, deletion and insertion rates achieves thebest rescoring results. On the other hand, even though the associatedperplexity measure is stable, it gives no better predictionof the final error rate than the vanilla one. Our best augmentationscheme increases the WER improvement from second-passrescoring from 1.1% to 1.9% absolute on the CHiMe-6 challenge.
Keywords
data augmentation, error simulation, languagemodeling, automatic speech recognition
Authors
BENEŠ, K.; BURGET, L.
Released
30. 8. 2021
Publisher
International Speech Communication Association
Location
Brno
ISBN
1990-9772
Periodical
Proceedings of Interspeech
Year of study
2021
Number
8
State
French Republic
Pages from
1872
Pages to
1876
Pages count
5
URL
BibTex
@inproceedings{BUT175841,
author="Karel {Beneš} and Lukáš {Burget}",
title="Text Augmentation for Language Models in High Error Recognition Scenario",
booktitle="Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH",
year="2021",
journal="Proceedings of Interspeech",
volume="2021",
number="8",
pages="1872--1876",
publisher="International Speech Communication Association",
address="Brno",
doi="10.21437/Interspeech.2021-627",
issn="1990-9772",
url="https://www.isca-speech.org/archive/interspeech_2021/benes21_interspeech.html"
}Documents