梳理乌尔都语语音识别技术演进，助力低资源语言研究
From Statistical Methods to Pre-Trained Models; A Survey on Automatic Speech Recognition for Resource Scarce Urdu Language
- 从统计方法到预训练模型，系统回顾乌尔都语语音识别发展脉络
- 分析现有数据集与算法，揭示低资源语言建模的关键瓶颈
- 为关注小语种语音处理的研究者提供方向指引
近年来，自动语音识别（ASR）技术取得了显著进展，深刻改变了人机交互方式。尽管主流语言已受益于这些进步，但像乌尔都语这样使用广泛却资源匮乏的语言仍面临独特挑战。本文全面探讨了乌尔都语语音识别研究的动态格局，重点聚焦于资源受限的乌尔都语场景，该语言在南亚多国广泛使用。文章梳理了当前研究趋势、技术演进及未来研究方向，旨在为有意投身此领域的研究人员提供指引。通过利用现代技术、分析现有数据集，并评估有效算法与工具，本文揭示了乌尔都语语言处理的独特挑战与机遇，推动其融入更广泛的语音研究体系。
原文摘要 · Abstract (English)
Automatic Speech Recognition (ASR) technology has witnessed significant advancements in recent years, revolutionizing human-computer interactions. While major languages have benefited from these developments, lesser-resourced languages like Urdu face unique challenges. This paper provides an extensive exploration of the dynamic landscape of ASR research, focusing particularly on the resource-constrained Urdu language, which is widely spoken across South Asian nations. It outlines current research trends, technological advancements, and potential directions for future studies in Urdu ASR, aiming to pave the way for forthcoming researchers interested in this domain. By leveraging contemporary technologies, analyzing existing datasets, and evaluating effective algorithms and tools, the paper seeks to shed light on the unique challenges and opportunities associated with Urdu language processing and its integration into the broader field of speech research.
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