low-resource

This research investigates cross-lingual transfer learning from Sinhala to Dhivehi for automatic speech recognition, achieving significant improvements in word error rate compared to Dhivehi-only baselines.

This paper proposes CoPiT, a cognitively motivated pivot-based translation pipeline for digraphic Mongolian that routes translation through the better-resourced Cyrillic script to improve translation from the low-resource Traditional script, achieving significant BLEU and COMET gains and releasing a new multi-script parallel dataset.

This paper proposes a multimodal framework that jointly improves Automatic Speech Recognition (ASR) and Dialect Identification (DID) for Indian languages, using a Bottleneck Encoder and RoBERTa with a gating mechanism. Evaluated on eight languages with 33 dialects, it achieves 81.63% DID accuracy and reduces CER/WER to 4.65%/17.73%.

Small AI models are proving valuable in regions with unreliable networks, enabling life-saving applications like counterfeit drug detection and disease identification in crops without needing constant internet connectivity.

PAST-TIDE is a stance detection system for the StanceNakba Shared Task, using statement tuning with cloze-style masked language modeling, prototypical contrastive learning, and topic-conditional layer normalization for cross-topic Arabic stance detection, achieving macro-F1 scores of 0.75 and 0.74 on subtasks A and B.

An 18-year-old Tunisian student introduces an open-source machine translation pipeline and parallel corpus for Tunisian Darija in Arabizi script, built from scratch with a small 15.6M-parameter Transformer and an honest baseline BLEU of 3.89, and calls for contributors to ethically expand the corpus.

This paper analyzes the use of LLM-as-a-Judge in multilingual and low-resource settings, finding inconsistent evaluation outcomes and overtrust in LLM judgments, and provides recommendations for better practices.

SPARCLE is a speaker-aware grapheme representation model that uses contrastive learning to align grapheme embeddings with acoustic representations, improving text-to-speech quality especially in low-resource settings.

This paper investigates cross-lingual relation extraction for Romanian by translating the SemEval-2010 Task 8 benchmark and evaluating Gemma 4 under zero-shot, few-shot, and QLoRA fine-tuning, comparing with smaller encoder baselines.

This paper proposes a tone-conditioned curriculum learning framework for low-resource Bantu speech recognition, combining hybrid difficulty scoring, gated adapters, and staged curriculum training. Evaluations on six Southern Bantu languages show that W2V-BERT outperforms Whisper on Nguni languages while Whisper performs better on Sotho-Tswana languages.

This paper introduces sinhala-ocr-lk-acts-1010, the first publicly available real-world page-level dataset for Sinhala OCR, and fine-tunes three vision language models (DeepSeek-OCR V1, DeepSeek-OCR V2, LightOnOCR-2-1B) using QLoRA. LightOnOCR-2-1B achieves a CER of 1.05%, outperforming both open-source and commercial OCR models, and maintains consistent performance across degraded documents from different time periods.

This paper investigates the distributional gap between synthetic and real speech in LLM-based ASR systems, identifies where the LLM separates them, and proposes using layer-selection and RIR augmentation to match real-data baselines with less real data.

This paper proposes DysLexLens, a low-resource LLM framework for analyzing dyslexic learners' experiences with AI tools using online forum data, featuring dictionary-driven filtering, knowledge-graph reasoning, and evaluation metrics.

This paper investigates LoRA fine-tuning of the VoxCPM2 TTS model to improve quality for low-resource languages like Khmer, while showing no gain for Korean which the base model already handles well. The adapter yields significant MOS improvement for Khmer with minimal parameter training.

This paper presents NEST-V1, a proof-of-concept multimodal framework for generating emotion-conditioned Nepali Sign Language avatars from spoken input, achieving 81.1% ASR accuracy and 79.21% emotion recognition accuracy on a dataset of 600 audio samples from 50 speakers.

This paper presents a modular end-to-end speech-to-speech conversational system for the low-resource Algerian Dialect, integrating ASR, NLU, RAG, and TTS with dedicated datasets and fine-tuned models.

Presents a neural machine translation system for the severely under-resourced Tangkhul–English language pair, achieving strong BLEU, chrF++, BERTScore, and COMET scores using fine-tuned ByT5-large and mT5-small models.

The paper proposes a novel framework (CDDTLDA) using transfer learning and data augmentation to improve Chinese dialects discrimination under low-resource conditions, achieving state-of-the-art results on two benchmark corpora.

This study evaluates bilingual fine-tuning with language identification tokens for improving ASR in low-resource languages across nine diverse language pairs, finding that high LID accuracy is beneficial and that providing the LID token at inference can boost performance when LID accuracy is low.

This paper introduces the Call Playbook dataset for classifying real-world B2B conversations and proposes methods to distill examples into compact, interpretable task instructions, achieving 99% token reduction and up to 7% AUC improvement over traditional in-context learning.