SDS-200: A Swiss German Speech to Standard German Text Corpus

TL;DR

This paper introduces SDS-200, a comprehensive Swiss German speech corpus with annotations, enabling advancements in speech translation, dialect recognition, and synthesis, and demonstrates baseline models with promising results.

Contribution

The creation of SDS-200, a large annotated Swiss German speech dataset, and the development of baseline speech translation models using this corpus.

Findings

Baseline speech translation model achieves 30.3% WER and 53.1 BLEU.

Fine-tuned XLS-R model achieves 21.6% WER and 64.0 BLEU.

Corpus covers extensive Swiss-German dialect diversity.

Abstract

We present SDS-200, a corpus of Swiss German dialectal speech with Standard German text translations, annotated with dialect, age, and gender information of the speakers. The dataset allows for training speech translation, dialect recognition, and speech synthesis systems, among others. The data was collected using a web recording tool that is open to the public. Each participant was given a text in Standard German and asked to translate it to their Swiss German dialect before recording it. To increase the corpus quality, recordings were validated by other participants. The data consists of 200 hours of speech by around 4000 different speakers and covers a large part of the Swiss-German dialect landscape. We release SDS-200 alongside a baseline speech translation model, which achieves a word error rate (WER) of 30.3 and a BLEU score of 53.1 on the SDS-200 test set. Furthermore, we use SDS-200 to fine-tune a pre-trained XLS-R model, achieving 21.6 WER and 64.0 BLEU.