Papers by Manuela Hürlimann
CEASR: A Corpus for Evaluating Automatic Speech Recognition (2020.lrec-1)
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Malgorzata Anna Ulasik, Manuela Hürlimann, Fabian Germann, Esin Gedik, Fernando Benites, Mark Cieliebak
| Challenge: | Automatic Speech Recognition (ASR) systems are increasingly needed for research and practical applications. |
| Approach: | They propose to use public speech corpora to evaluate the quality of automatic speech recognition (ASR) they calculate an average Word Error Rate (WER) per corpus, per system and per corpor-system pair . |
| Outcome: | The proposed corpus evaluates the quality of automatic speech recognition systems using public speech corpora and transcripts generated by state-of-the-art systems. |
SDS-200: A Swiss German Speech to Standard German Text Corpus (2022.lrec-1)
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Michel Plüss, Manuela Hürlimann, Marc Cuny, Alla Stöckli, Nikolaos Kapotis, Julia Hartmann, Malgorzata Anna Ulasik, Christian Scheller, Yanick Schraner, Amit Jain, Jan Deriu, Mark Cieliebak, Manfred Vogel
| Challenge: | Using a web recording tool, participants were asked to translate their Swiss German text to their own dialect before recording it. |
| Approach: | They present a corpus of Swiss German dialectal speech with Standard German text translations . the dataset allows for training speech translation, dialect recognition, and speech synthesis systems . |
| Outcome: | The dataset allows for training speech translation, dialect recognition, and speech synthesis systems. |
Dialect Transfer for Swiss German Speech Translation (2023.findings-emnlp)
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| Challenge: | a study of Swiss German speech translation systems focuses on dialect diversity and differences between Swiss German and Standard German. |
| Approach: | They focus on the impact of dialect diversity and differences between Swiss German and Standard German . they first review the Swiss German dialect landscape and the differences to Standard German. |
| Outcome: | The proposed model is based on the Swiss German dialect landscape and differences to Standard German. |
STT4SG-350: A Speech Corpus for All Swiss German Dialect Regions (2023.acl-short)
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Michel Plüss, Jan Deriu, Yanick Schraner, Claudio Paonessa, Julia Hartmann, Larissa Schmidt, Christian Scheller, Manuela Hürlimann, Tanja Samardžić, Manfred Vogel, Mark Cieliebak
| Challenge: | We present a corpus of Swiss German speech annotated with Standard German text at the sentence level. |
| Approach: | They present a corpus of Swiss German speech annotated with Standard German sentences . they use a web app to show the speakers standard German sentences and record them . |
| Outcome: | The corpus contains 343 hours of speech from all Swiss German dialect regions . it is the largest public speech corpus for Swiss German to date . |
Error-preserving Automatic Speech Recognition of Young English Learners’ Language (2024.acl-long)
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| Challenge: | State-of-the-art speech recognition models are often trained on adult read-aloud data by native speakers and do not transfer well to young language learners’ speech. |
| Approach: | They propose to use an automated speech recognition module to train language learners' speaking skills on spontaneous speech by young language learners. |
| Outcome: | The proposed model improves on 85 hours of English audio spoken by Swiss learners and preserves their mistakes. |