Seven Akan ASR models were evaluated across four domains, showing strong in-domain performance but weak generalization, with Whisper and Wav2Vec2 producing distinct error types.
BibleTTS: a large, high-fidelity, multilingual, and uniquely African speech corpus
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
BibleTTS is a large, high-quality, open speech dataset for ten languages spoken in Sub-Saharan Africa. The corpus contains up to 86 hours of aligned, studio quality 48kHz single speaker recordings per language, enabling the development of high-quality text-to-speech models. The ten languages represented are: Akuapem Twi, Asante Twi, Chichewa, Ewe, Hausa, Kikuyu, Lingala, Luganda, Luo, and Yoruba. This corpus is a derivative work of Bible recordings made and released by the Open.Bible project from Biblica. We have aligned, cleaned, and filtered the original recordings, and additionally hand-checked a subset of the alignments for each language. We present results for text-to-speech models with Coqui TTS. The data is released under a commercial-friendly CC-BY-SA license.
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Benchmarking Akan ASR Models Across Domain-Specific Datasets: A Comparative Evaluation of Performance, Scalability, and Adaptability
Seven Akan ASR models were evaluated across four domains, showing strong in-domain performance but weak generalization, with Whisper and Wav2Vec2 producing distinct error types.