A Literature Review of Keyword Spotting Technologies for Urdu

TL;DR

This review analyzes the evolution of keyword spotting technologies for Urdu, a low-resource language with complex phonetics, emphasizing recent neural approaches and the need for tailored solutions to improve performance in multilingual and resource-constrained contexts.

Contribution

It provides a comprehensive overview of technological advancements in Urdu KWS, highlighting the shift from traditional models to neural architectures and identifying gaps for future research.

Findings

Neural models outperform traditional GMMs in Urdu KWS

Multi-task learning and self-supervised methods enhance performance

Urdu-specific challenges require tailored speech technology solutions

Abstract

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.