Hauptthemen unserer Publikationen zu Schweizerdeutsch:
2022
Forschungsberichte
Schraner, Yanick; Scheller, Christian; Plüss, Michel; Neukom, Lukas; Vogel, Manfred
Comparison of Unsupervised Learning and Supervised Learning with Noisy Labels for Low-Resource Speech Recognition Forschungsbericht
2022.
Links | BibTeX | Schlagwörter: forced-alignment, low-resource, self-supervised, semi-supervised, Speech Recognition/Understanding, speech translation
@techreport{nokey,
title = {Comparison of Unsupervised Learning and Supervised Learning with Noisy Labels for Low-Resource Speech Recognition},
author = {Yanick Schraner and Christian Scheller and Michel Plüss and Lukas Neukom and Manfred Vogel},
editor = {University of Applied Sciences and Arts Northwestern Switzerland},
url = {https://www.isca-speech.org/archive/pdfs/interspeech_2022/schraner22_interspeech.pdf},
year = {2022},
date = {2022-09-22},
keywords = {forced-alignment, low-resource, self-supervised, semi-supervised, Speech Recognition/Understanding, speech translation},
pubstate = {published},
tppubtype = {techreport}
}
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%.
Forschungsberichte
Plüss, Michel; Neukom, Lukas; Vogel, Manfred
GermEval 2020 Task 4: Low-Resource Speech-to-Text Forschungsbericht
2020.
Abstract | Links | BibTeX | Schlagwörter: low-resource, speech translation, Speech-to-Text
@techreport{nokey,
title = {GermEval 2020 Task 4: Low-Resource Speech-to-Text},
author = {Michel Plüss and Lukas Neukom and Manfred Vogel},
editor = {Institute for Data Science
University of Applied Sciences and Arts Northwestern Switzerland
Windisch, Switzerland},
url = {https://ceur-ws.org/Vol-2624/germeval-task4-paper1.pdf},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
abstract = {We present the results and findings of GermEval 2020 Task 4 on Low-Resource Speech-to-Text. Participants were asked to build a system translating Swiss German speech to Standard German text and minimize its word error rate. The task was based on a new dataset for Swiss German to Standard German speech translation, which contains 74 hours of sentence-level speech-text-pairs. 3 teams participated, with the winning contribution reaching a word error rate of 40.29 %.
},
keywords = {low-resource, speech translation, Speech-to-Text},
pubstate = {published},
tppubtype = {techreport}
}
We present the results and findings of GermEval 2020 Task 4 on Low-Resource Speech-to-Text. Participants were asked to build a system translating Swiss German speech to Standard German text and minimize its word error rate. The task was based on a new dataset for Swiss German to Standard German speech translation, which contains 74 hours of sentence-level speech-text-pairs. 3 teams participated, with the winning contribution reaching a word error rate of 40.29 %.