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2020
Dokumentationen
Büchi, Matthias; Ulasik, Malgorzata Anna; Hürlimann, Manuela; Benites, Fernando; von Däniken, Pius; Cieliebak, Mark
ZHAW-InIT at GermEval 2020 Task 4: Low-Resource Speech-to-Text Dokumentation
at GermEval, Low-Resource Speech-to-Text Shared Task (Hrsg.): 2020, ISSN: 1613-0073.
Abstract | Links | BibTeX | Schlagwörter: CNN, low-resource, speech translation, Speech-to-Text
@manual{nokey,
title = {ZHAW-InIT at GermEval 2020 Task 4: Low-Resource Speech-to-Text },
author = {Matthias Büchi and Malgorzata Anna Ulasik and Manuela Hürlimann and Fernando Benites and Pius von Däniken and Mark Cieliebak},
editor = {Low-Resource Speech-to-Text Shared Task at GermEval},
url = {https://doi.org/10.21256/zhaw-21550
https://digitalcollection.zhaw.ch/handle/11475/21550},
doi = {10.21256/zhaw-21550},
issn = {1613-0073},
year = {2020},
date = {2020-06-01},
urldate = {2020-06-01},
abstract = {This paper presents the contribution of ZHAW-InIT to Task 4 ”Low-Resource STT” at GermEval 2020. The goal of the task is to develop a system for translating Swiss German dialect speech into Standard German text in the domain of parliamentary debates. Our approach is based on Jasper, a CNN Acoustic Model, which we fine-tune on the task data. We enhance the base system with an extended Language Model containing in-domain data and speed perturbation and run further experiments with post-processing. Our submission achieved first place with a final Word Error Rate of 40.29%.},
keywords = {CNN, low-resource, speech translation, Speech-to-Text},
pubstate = {published},
tppubtype = {manual}
}
This paper presents the contribution of ZHAW-InIT to Task 4 ”Low-Resource STT” at GermEval 2020. The goal of the task is to develop a system for translating Swiss German dialect speech into Standard German text in the domain of parliamentary debates. Our approach is based on Jasper, a CNN Acoustic Model, which we fine-tune on the task data. We enhance the base system with an extended Language Model containing in-domain data and speed perturbation and run further experiments with post-processing. Our submission achieved first place with a final Word Error Rate of 40.29%.