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This paper introduces SpeakPay and a Nepali financial speech dataset, showing that LoRA fine-tuning of Whisper reduces Word Error Rate by 67.2% and improves transaction success rates for low-resource language accessibility.
This paper presents a controlled benchmark comparing six multilingual pre-trained ASR models on Nepali speech, finding Whisper-Large-v3-Turbo and IndicWav2Vec perform best, while CTC decoders offer up to 29x faster inference. It provides the first standardized efficiency-aware reference numbers for Nepali ASR.
This paper describes a two-stage vision-language adaptation system for Nepali meme classification, using Qwen3-VL-8B-Instruct with LoRA fine-tuning and contrastive learning. The system achieved 2nd place in hate speech detection and 4th in sentiment analysis at the CHiPSAL 2026 shared task.
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.