针对乌尔都语语音识别难题，梳理了关键词检测技术的演进与挑战。
A Literature Review of Keyword Spotting Technologies for Urdu
- 从高斯混合模型到深度神经网络与变换器，系统梳理技术发展路径
- 强调多任务学习与自监督方法在低资源语言中的关键作用
- 适合关注低资源语言语音技术研究的学者和开发者
本文综述了关键词检测（KWS）技术的发展，特别聚焦于巴基斯坦的低资源语言（LRL）乌尔都语。尽管全球语音技术取得显著进展，乌尔都语因复杂的语音特征仍面临独特挑战，亟需定制化解决方案。文章回顾了从基础高斯混合模型到深度神经网络、变换器等先进神经架构的演进历程，重点指出多任务学习与利用无标签数据的自监督方法带来的关键突破。同时探讨了新兴技术在多语言和资源受限环境下的应用潜力，强调必须开展面向乌尔都语及其类似语言的上下文特定研究，以推动更包容的语音技术发展。
原文摘要 · Abstract (English)
This literature review surveys the advancements of keyword spotting (KWS) technologies, specifically focusing on Urdu, Pakistan's low-resource language (LRL), which has complex phonetics. Despite the global strides in speech technology, Urdu presents unique challenges requiring more tailored solutions. The review traces the evolution from foundational Gaussian Mixture Models to sophisticated neural architectures like deep neural networks and transformers, highlighting significant milestones such as integrating multi-task learning and self-supervised approaches that leverage unlabeled data. It examines emerging technologies' role in enhancing KWS systems' performance within multilingual and resource-constrained settings, emphasizing the need for innovations that cater to languages like Urdu. Thus, this review underscores the need for context-specific research addressing the inherent complexities of Urdu and similar URLs and the means of regions communicating through such languages for a more inclusive approach to speech technology.
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