Abstract
Nepal Bhasha (Newari), an endangered language of the Kathmandu Valley, remains digitally marginalized due to the severe scarcity of annotated speech resources. In this work, we introduce NwÄchÄ MunÄ, a newly curated 5.39-hour manually transcribed Devanagari speech corpus for Nepal Bhasha, and establish the first benchmark using script-preserving acoustic modeling. We investigate whether proximal cross-lingual transfer from a geographically and linguistically adjacent language (Nepali) can rival large-scale multilingual pretraining in an ultra-low-resource Automatic Speech Recognition (ASR) setting. Fine-tuning a Nepali Conformer model reduces the Character Error Rate (CER) from a 52.54% zero-shot baseline to 17.59% with data augmentation, effectively matching the performance of the multilingual Whisper-Small model despite utilizing significantly fewer parameters. Our findings demonstrate that proximal transfer within South Asian language clusters serves as a computationally efficient alternative to massive multilingual models. We openly release the dataset and benchmarks to digitally enable the Newari community and foster further research in Nepal Bhasha.
Abstract (translated)
尼泊尔语（涅瓦利语，Newari）是加德满都谷地的一种濒危语言，由于标注语音资源极度稀缺，其在数字领域一直处于边缘地位。在这项工作中，我们引入了Nwāchā Muna，这是一个新整理的5.39小时的手动转写德瓦那格里语语音数据集，用于尼泊尔语，并建立了第一个使用保持书写形式声学建模基准。本研究探讨了从地理位置和语言上相邻的语言（尼泊尔语）进行邻近跨语言转移，是否能在极度低资源的自动语音识别(ASR)设置中与大规模多语言预训练相匹敌。通过对尼泊尔语Conformer模型进行微调，在使用数据增强的情况下，字符错误率(CER)从零样本基线52.54%降低到17.59%，尽管使用的参数显著较少，但其性能几乎与多语言Whisper-Small模型相当。我们的研究结果表明，在南亚语系中进行邻近转移是大规模多语言模型的一种计算效率更高的替代方案。我们公开发布了该数据集和基准测试，以在数字领域为涅瓦利社区赋能，并促进尼泊尔语进一步的研究。
URL
https://arxiv.org/abs/2603.07554
