A Literature Review of Keyword Spotting Technologies for Urdu
It addresses the challenge of developing inclusive speech technology for Urdu speakers, but is incremental as it synthesizes existing research rather than presenting new findings.
This literature review examines keyword spotting technologies for Urdu, a low-resource language with complex phonetics, tracing advancements from Gaussian Mixture Models to neural architectures like transformers and highlighting the need for tailored solutions.
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.