low-resource-languages
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- PaperarXiv — Language & NLP (cs.CL)5 Jul 2026
Towards Digital Preservation of Efik: TTS for a Low-Resource African Language
Offiong Bassey Edet, Emmanuel Oyo-Ita, Archibong Okon Archibong, David Effanga Bassey et al.
Presents the first end-to-end text-to-speech study for Efik, a low-resource tonal language, comparing four neural models with a curated corpus. MMS-TTS achieved the highest mean opinion score of 3.80, though tonal errors persisted, highlighting the need for tone-aware modeling.
Original abstract
Efik, a tonal language spoken by about 3 million second language speakers and 1.5 million native speakers in Southeastern Nigeria, remains underrepresented in speech synthesis research. We present the first documented end-to-end text-to-speech study for Efik, introducing a curated single speaker corpus of 2,632 utterances totaling three hours and a comparative evaluation of four neural models (VITS, MMS-TTS, SpeechT5, and Orpheus-TTS) under low resource conditions. Native speakers evaluated the systems using MOS, Nat-MOS, and A-MOS. MMS-TTS achieved the highest MOS of 3.80 +/- 0.63 and produced more stable long form speech, though tonal errors persisted. Other models showed greater tonal and prosodic inconsistencies. These results provide a reproducible baseline and highlight the need for larger corpora and tone aware modeling for tonal African languages.
- PaperarXiv — Language & NLP (cs.CL)21 May 2026
From Lexicon to AI: A Structured-Data Pipeline for Specialized Conversational Systems in Low-Resource Languages
Siddhant Hitesh Mantri, Dhara Gorasiya, Malhar Kulkarni, Pushpak Bhattacharya
Researchers transformed Hindi WordNet into 1.25 million instruction-response pairs to fine-tune a 12B-parameter language model, creating a Hindi language learning chatbot. The system achieved higher pedagogical effectiveness (91.0) than general-purpose models (79.4-83.6), demonstrating that structured linguistic resources can enable specialized conversational AI for low-resource languages.
Original abstract
Low-resource languages face a critical challenge in AI development: creating specialized conversational systems without access to massive training corpora. We present a systematic methodology for transforming structured linguistic resources into specialized AI systems, demonstrating that expert-curated lexical databases can serve as effective foundations for conversational AI development. Our approach converts Hindi WordNet into 1.25 million diverse instruction-response pairs, fine-tunes a 12B-parameter language model using resource-efficient LoRA with 4-bit quantization. Evaluation through a Hindi language learning chatbot demonstrates that structured-knowledge-based systems achieve superior pedagogical effectiveness (91.0 vs. 79.4-83.6 for general-purpose models) while maintaining competitive semantic performance and exceptional consistency. The complete pipeline demonstrates a proof-of-concept methodology using Hindi for developing specialized AI systems for any languages with WordNet resources. This work addresses the critical gap in AI accessibility for low-resource languages, offering a practical alternative to corpus-intensive approaches and potentially enabling specialized AI development for the hundreds of languages with existing WordNet resources.