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This paper addresses a critical bottleneck in AI development by releasing Nord-Parl-TTS, a large-scale dataset of 900 hours of Finnish and 5090 hours of Swedish parliamentary speech designed to train text-to-speech models. By leveraging existing data processing pipelines like Emilia, the authors provide open resources that help narrow the resource gap for lower-resourced languages in the TTS domain.
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Nord-Parl-TTS: Finnish and Swedish TTS Dataset from Parliament Speech

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2509.17988v2 Announce Type: cross Abstract: Text-to-speech (TTS) development is limited by scarcity of high-quality, publicly available speech data for most languages outside a few high-resource languages. We present Nord-Parl-TTS, an open TTS dataset for Finnish and Swedish based on speech found in the wild. Using recordings of Nordic parliamentary proceedings, we extract 900 hours of Finnish and 5090 hours of Swedish speech suitable for TTS training. The dataset is built using an adapted version of the Emilia data processing pipeline and includes unified evaluation sets to support model development and benchmarking. By offering open, large-scale data for Finnish and Swedish, Nord-Parl-TTS narrows the resource gap in TTS between high- and lower-resourced languages.

Abstract:Text-to-speech (TTS) development is limited by scarcity of high-quality, publicly available speech data for most languages outside a few high-resource languages. We present Nord-Parl-TTS, an open TTS dataset for Finnish and Swedish based on speech found in the wild. Using recordings of Nordic parliamentary proceedings, we extract 900 hours of Finnish and 5090 hours of Swedish speech suitable for TTS training. The dataset is built using an adapted version of the Emilia data processing pipeline and includes unified evaluation sets to support model development and benchmarking. By offering open, large-scale data for Finnish and Swedish, Nord-Parl-TTS narrows the resource gap in TTS between high- and lower-resourced languages.

Accepted by ICASSP 2026. 5 pages, 2 figures

Audio and Speech Processing (eess.AS); Computation and Language (cs.CL)

arXiv:2509.17988 [eess.AS]

(or arXiv:2509.17988v2 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2509.17988 Focus to learn more arXiv-issued DOI via DataCite

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