Papers with Swiss 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 . |
Strategies and Challenges for Crowdsourcing Regional Dialect Perception Data for Swiss German and Swiss French (L18-1)
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| Challenge: | a crowdsourcing project in the field of Swiss German dialects and Swiss French accents collects linguistic data. |
| Approach: | a gamified crowdsourcing platform was set up to collect linguistic data on Swiss German and Swiss French accents. |
| Outcome: | a gamified crowdsourcing platform collects linguistic data on Swiss German and Swiss French accents . the platform has provided 470,000 localizations, with 7,500 registered users and 30,000 anonymous visitors . |
A Swiss German Dictionary: Variation in Speech and Writing (2020.lrec-1)
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Larissa Schmidt, Lucy Linder, Sandra Djambazovska, Alexandros Lazaridis, Tanja Samardžić, Claudiu Musat
| Challenge: | Besides standard German, Swiss German is spoken in about two thirds of Switzerland. |
| Approach: | They propose a dictionary containing normalized forms of common Swiss German words paired with Swiss German phonetic transcriptions to alleviate the uncertainty associated with this diversity. |
| Outcome: | The proposed dictionary is the first to combine spontaneous translation and phonetic transcriptions in large-scale, scalable phoneme to grapheme model that generates credible novel Swiss German writings. |
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. |
SB-CH: A Swiss German Corpus with Sentiment Annotations (L18-1)
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| Challenge: | Using sentiment annotations, we find no corpus for written Swiss German, which is considered low-resourced due to its non-official status and phonetic differences. |
| Approach: | They propose to annotate a Swiss German corpus with sentiment annotations for sentiment analysis using Facebook comments and online chats. |
| Outcome: | The proposed corpus consists of more than 200,000 phrases and 1843 phrases with labels positive, negative, or neutral. |
Early Guessing for Dialect Identification (2022.findings-emnlp)
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| Challenge: | Current research on dialect identification is model-centric, focusing on performance. |
| Approach: | They propose a data-centric approach to find the shortest input needed to make a plausible guess. |
| Outcome: | The proposed method generalizes across dialects and datasets with two shortening criteria. |
Quantifying the Dialect Gap and its Correlates Across Languages (2023.findings-emnlp)
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| Challenge: | Historically, studies investigating minority variants of languages have been limited to a select few languages. |
| Approach: | They evaluate state-of-the-art large language models for regional dialects of several high- and low-resource languages and analyze how regional dialect gap is correlated with economic, social, and linguistic factors. |
| Outcome: | The proposed model is compared with two high-use applications and shows that it can solve the regional dialect gap. |
Data-Driven Pronunciation Modeling of Swiss German Dialectal Speech for Automatic Speech Recognition (L18-1)
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| Challenge: | a Swiss German speech recognizer is trained using a standard German annotation model. |
| Approach: | They propose to train a Swiss German speech recognition system using a standard German annotation model. |
| Outcome: | The proposed system is based on a standard German annotation model and a grapheme-to-phoneme conversion model. |
Machine Translation of Low-Resource Spoken Dialects: Strategies for Normalizing Swiss German (L18-1)
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| Challenge: | Using character-based neural MT, we normalize Swiss German input to address regional diversity. |
| Approach: | They propose to use character-based neural MT to normalize Swiss German input and phrase-based statistical MT for a low-resource family of dialects. |
| Outcome: | The proposed system achieves 36% BLEU score when translating from the Bernese dialect. |
Dialect-to-Standard Normalization: A Large-Scale Multilingual Evaluation (2023.findings-emnlp)
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| Challenge: | Text normalization is a range of tasks that consist in replacing non-standard spellings with their standard equivalents. |
| Approach: | They introduce dialect-to-standard normalization as a sentence-level character transduction task and provide a large-scale analysis of these methods. |
| Outcome: | The proposed model performs best for Finnish, Swiss German and Slovene while the pre-trained model using full sentences performs the best for Norwegian. |
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. |