LuxEmo: Expressive Text-to-Speech Corpus for Luxembourgish
Score
53Why selected
Moderate corpus size, but systematic methodology, significant contribution to low-resource TTS, and multiple baselines.
AI commentary
「This work fills the gap of high-quality emotional speech corpus for low-resource languages and provides a reproducible semi-automatic construction framework.」
Quick overview
LuxEmo is a 21-hour conversational expressive speech corpus for Luxembourgish with 4 emotion categories, derived from RTL youth broadcasts via semi-automatic curation combining automated detection and human validation. The workflow includes VAD, denoising, language identification, ASR segmentation, emotion prediction, and lexical cues. Five expressive TTS systems (cross-lingual, multilingual, adaptation, non-parametric prosody transfer) are benchmarked using objective metrics and human evaluation.
- First emotional speech dataset for Luxembourgish: 21 hours, 4 emotion categories.
- Semi-automatic curation workflow integrating multiple detection steps and human validation.
- Benchmarks five TTS approaches: cross-lingual, multilingual, adaptation, and non-parametric prosody transfer.
Full text
Computer Science > Computation and Language
arXiv:2606.31947v1 (cs) [Submitted on 30 Jun 2026 (this version), latest version 2 Jul 2026 (v2)]Title:LuxEmo: Expressive Text-to-Speech Corpus for Luxembourgish
Authors:Nina Hosseini-Kivanani, Sandipana Dowerah View a PDF of the paper titled LuxEmo: Expressive Text-to-Speech Corpus for Luxembourgish, by Nina Hosseini-Kivanani and Sandipana Dowerah View PDF HTML (experimental)Abstract:State-of-the-art speech datasets predominantly focus on widely spoken languages, often overlooking low-resource languages such as Luxembourgish, which remain underrepresented in speech technology research. In this work, we introduce LuxEmo, a 21-hour conversational expressive speech corpus for Luxembourgish with 4 emotion categories. LuxEmo is derived from Radio Télévision Luxembourg (RTL) youth broadcasts, using automated detection followed by human validation. We propose a semi-automatic curation workflow combining voice activity detection, denoising, language identification, LuxASR-based segmentation, automatic emotion prediction, lexical cues, and targeted human review. Additionally, we benchmark five expressive TTS systems covering German-based cross-lingual transfer, multilingual Luxembourgish support, Luxembourgish adaptation, and non-parametric prosody transfer. Performance is evaluated using both objective metrics and human evaluation.
| Comments: | 7 pages, 4 figures, under review |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.31947 [cs.CL] |
| (or arXiv:2606.31947v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.31947 Focus to learn more arXiv-issued DOI via DataCite |
Submission history
From: Nina Hosseini-Kivanani [view email][v1] Tue, 30 Jun 2026 16:53:15 UTC (2,328 KB)
[v2] Thu, 2 Jul 2026 16:10:55 UTC (2,327 KB)
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