2024/09/16 by Syed A. Rizvi, Rizvi, Syed Muhammad Aqdas
Computer Science · Decision Sciences · #Advanced Text Analysis Techniques #Artificial Intelligence (cs.AI) #Audio and Speech Processing (eess.AS) #Computation and Language (cs.CL) #FOS: Computer and information sciences #FOS: Electrical engineering #ICT in Developing Communities #Machine Learning (cs.LG) #Sound (cs.SD) #Technology Adoption and User Behaviour #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2409.16317
openalex publication_date 2024/09/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.