计算机工程与应用2025，Vol.61Issue(4)：59-71,13.DOI:10.3778/j.issn.1002-8331.2405-0425
面向低数据资源的语音识别研究综述
Review of Speech Recognition Techniques for Low Data Resources
摘要
Abstract
Recently,the focus of automatic speech recognition has shifted from traditional methods to speech recognition methods based on deep learning.Moreover,the"large model"phenomenon reflects that the performance of deep learning methods significantly improves as the volume of training data increases.However,real-world complexity,uneven speech data distribution,and privacy concerns challenge data collection.Additionally,the annotation of speech data requires the involvement of a large number of professionals,leading to high labeling costs.Therefore,speech recognition often faces the issue of insufficient data resources in practical applications.Building a high-performing and stable speech recognition system under low data resource conditions remains a research challenge.Consequently,this paper briefly summarizes the development history of speech recognition,then outlines the basic framework of speech recognition and common open-source datasets at home and abroad.Focusing on the low data resource issue,this paper analyzes the methods for deter-mining low data resources in detail,and then reviews four categories of technical solutions,including data augmentation,federated learning,self-supervised learning,and meta-learning,provides a systematic analysis of their performance status and advantages and disadvantages.Finally,this paper discusses the potential future development trends and possible chal-lenges faced by this research direction.关键词
语音识别/低数据资源/数据增强/联邦学习/自监督学习/元学习Key words
speech recognition/low data resources/data augmentation/federated learning/self-supervised learning/meta-learning分类
信息技术与安全科学引用本文复制引用
许春冬,吴子煜,葛凤培..面向低数据资源的语音识别研究综述[J].计算机工程与应用,2025,61(4):59-71,13.基金项目
国家自然科学基金(12204062,11864016) （12204062,11864016）
江西省教育厅项目. （）
