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.

Similar Papers

STT4SG-350: A Speech Corpus for All Swiss German Dialect Regions (2023.acl-short)

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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 .
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.
SDS-200: A Swiss German Speech to Standard German Text Corpus (2022.lrec-1)

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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.
Standard German Subtitling of Swiss German TV content: the PASSAGE Project (2022.lrec-1)

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Challenge: In Switzerland, two thirds of the population speak Swiss German, a primarily spoken language with no standardised written form.
Approach: They propose to combine a speech recognition system with an intralingual machine translation system to automate the subtitling process.
Outcome: The proposed systems improve the quality of the standardized Swiss German subtitles but are not capable of producing correct Standard German.
A Swiss German Dictionary: Variation in Speech and Writing (2020.lrec-1)

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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.
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.
LibriS2S: A German-English Speech-to-Speech Translation Corpus (2022.lrec-1)

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Challenge: Recent advances in speech-to-text translation have led to significant improvements, but the availability of appropriate training data is limiting.
Approach: They propose a new text-to-speech and speech-tospech translation model that directly learns to generate the speech signal based on the pronunciation of the source language.
Outcome: The proposed model learns to generate speech signal based on pronunciation of source language.
Using Automatic Speech Recognition in Spoken Corpus Curation (2020.lrec-1)

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Challenge: Automatic Speech Recognition (ASR) is a new way to make audio-visual data accessible.
Approach: They propose to use automatic speech recognition (ASR) to make audio-visual data accessible by systematic queries.
Outcome: The proposed system has higher recognition scores for the north of Germany vs. lower scores for south of the country.
LibriVoxDeEn: A Corpus for German-to-English Speech Translation and German Speech Recognition (2020.lrec-1)

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Challenge: a corpus of sentence-aligned triples of German audio, German text, and English translation is available for speech recognition . a large corpus is available to date for end-to-end speech translation based on parallel data .
Approach: They present a corpus of sentence-aligned triples of German audio, German text, and English translation based on German audio books.
Outcome: The proposed corpus is the largest resource for German speech recognition and for end-to-end German-to English speech translation.
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 .

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