Saar-Voice: A Multi-Speaker Saarbrücken Dialect Speech Corpus
For researchers in dialectal speech processing, this corpus fills a gap in resources for a specific German dialect, but the contribution is incremental as it primarily provides data rather than novel methods.
The authors introduce Saar-Voice, a six-hour multi-speaker speech corpus for the Saarbrücken dialect of German, addressing the underrepresentation of dialects in NLP. The corpus provides aligned text and audio, serving as a foundation for dialect-aware TTS in low-resource settings.
Natural language processing (NLP) and speech technologies have made significant progress in recent years; however, they remain largely focused on standardized language varieties. Dialects, despite their cultural significance and widespread use, are underrepresented in linguistic resources and computational models, resulting in performance disparities. To address this gap, we introduce Saar-Voice, a six-hour speech corpus for the Saarbrücken dialect of German. The dataset was created by first collecting text through digitized books and locally sourced materials. A subset of this text was recorded by nine speakers, and we conducted analyses on both the textual and speech components to assess the dataset's characteristics and quality. We discuss methodological challenges related to orthographic and speaker variation, and explore grapheme-to-phoneme (G2P) conversion. The resulting corpus provides aligned textual and audio representations. This serves as a foundation for future research on dialect-aware text-to-speech (TTS), particularly in low-resource scenarios, including zero-shot and few-shot model adaptation.