Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-21550
Publication type: Conference paper
Type of review: Peer review (abstract)
Title: ZHAW-InIT at GermEval 2020 task 4 : low-resource speech-to-text
Authors: Büchi, Matthias
Ulasik, Malgorzata Anna
Hürlimann, Manuela
Benites de Azevedo e Souza, Fernando
von Däniken, Pius
Cieliebak, Mark
et. al: No
DOI: 10.21256/zhaw-21550
Proceedings: Proceedings of the 5th Swiss Text Analytics Conference (SwissText) & 16th Conference on Natural Language Processing (KONVENS)
Editors of the parent work: Ebling, Sarah
Tuggener, Don
Hürlimann, Manuela
Cieliebak, Mark
Volk, Martin
Conference details: 5th SwissText & 16th KONVENS Joint Conference, Zurich (online), 24-25 June 2020
Issue Date: Jun-2020
ISSN: 1613-0073
Language: English
Subject (DDC): 410.285: Computational linguistics
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%.
URI: https://digitalcollection.zhaw.ch/handle/11475/21550
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Appears in collections:Publikationen School of Engineering

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