STT4SG-350: A Speech Corpus for All Swiss German Dialect Regions

• URL: http://arxiv.org/abs/2305.18855v1
• Date: Tue, 30 May 2023 08:49:38 GMT
• Title: STT4SG-350: A Speech Corpus for All Swiss German Dialect Regions
• Authors: Michel Pl\"uss, Jan Deriu, Yanick Schraner, Claudio Paonessa, Julia Hartmann, Larissa Schmidt, Christian Scheller, Manuela H\"urlimann, Tanja Samard\v{z}i\'c, Manfred Vogel, Mark Cieliebak
• Abstract summary: We present STT4SG-350 (Speech-to-Text for Swiss German), a corpus of Swiss German speech annotated with Standard German text at the sentence level. The data is collected using a web app in which the speakers are shown Standard German sentences, which they translate to Swiss German and record. It contains 343 hours of speech from all dialect regions and is the largest public speech corpus for Swiss German to date.
• Score: 5.6787416472329495
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present STT4SG-350 (Speech-to-Text for Swiss German), a corpus of Swiss German speech, annotated with Standard German text at the sentence level. The data is collected using a web app in which the speakers are shown Standard German sentences, which they translate to Swiss German and record. We make the corpus publicly available. It contains 343 hours of speech from all dialect regions and is the largest public speech corpus for Swiss German to date. Application areas include automatic speech recognition (ASR), text-to-speech, dialect identification, and speaker recognition. Dialect information, age group, and gender of the 316 speakers are provided. Genders are equally represented and the corpus includes speakers of all ages. Roughly the same amount of speech is provided per dialect region, which makes the corpus ideally suited for experiments with speech technology for different dialects. We provide training, validation, and test splits of the data. The test set consists of the same spoken sentences for each dialect region and allows a fair evaluation of the quality of speech technologies in different dialects. We train an ASR model on the training set and achieve an average BLEU score of 74.7 on the test set. The model beats the best published BLEU scores on 2 other Swiss German ASR test sets, demonstrating the quality of the corpus.

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