中国科学数据（中英文网络版）2026，Vol.11Issue(1)：43-53,11.DOI:10.11922/11-6035.csd.2025.0113.zh
XBMU-AMDO31:藏语安多方言语音识别数据集
XBMU-AMDO31:A dataset of speech recognition dataset for the Amdo dialect of Tibetan
摘要
Abstract
In recent years,while significant progress has been made in speech recognition technology for high-resource languages(such as English and Mandarin),research on low-resource languages with complex phonologies,like Tibetan,has progressed relatively slow.As a low-resource and phonologically complex language,Amdo Tibetan faces dual challenges in speech recognition:data scarcity and insufficient quality and diversity of available datasets.The lack of publicly accessible datasets has imposed numerous constraints on related research.To address these challenges,this paper introduces and presents an open-source speech recognition dataset for the Amdo Tibetan dialect.The speech samples were initially collected in Xiahe County,Gansu Province,China,comprising 31 hours of recordings from 66 native speakers along with corresponding transcriptions.Subsequent manual quality control and standardization were applied to ensure the authenticity of the dialect as well as the consistency and quality of the data.All resources in this dataset have been made publicly available and have already been utilized in multiple research papers and studies on Tibetan speech recognition,receiving widespread acclaim from experts in the field—further validating the dataset's quality.This dataset serves as an important supplement to high-quality speech data for Amdo Tibetan,and provides unique support for cross-lingual transfer learning and few-shot speech technology research due to its complex phonological characteristics.关键词
语音识别/安多藏语数据集/多说话人/低资源Key words
speech recognition/Amdo Tibetan dataset/multi-speaker/low-resource引用本文复制引用
谢晨宇,李冠宇,马立克,孙倩,郭玉豪..XBMU-AMDO31:藏语安多方言语音识别数据集[J].中国科学数据（中英文网络版）,2026,11(1):43-53,11.基金项目
国家自然科学基金(61633013) （61633013）
2024年甘肃省科技重大专项计划项目(24ZDFA004). National Natural Science Foundation of China(61633013) （24ZDFA004）
Gansu Provincial Science and Technology Major Project(24ZDFA004) （24ZDFA004）
