# A Literature Review of Keyword Spotting Technologies for Urdu

arXiv: https://arxiv.org/abs/2409.16317 · HTML: https://mlsanity.com/paper/2409.16317/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 16 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Unified Keyword Spotting and Audio Tagging on Mobile Devices with Transformers](https://mlsanity.com/paper/2303.01812/) (2023)
- [HEiMDaL: Highly Efficient Method for Detection and Localization of wake-words](https://mlsanity.com/paper/2210.15425/) (2022)
- [Improving vision-inspired keyword spotting using dynamic module skipping in streaming conformer encoder](https://mlsanity.com/paper/2309.00140/) (2023)

## Built on by (most cited first, 1 of 1)

- [BaltiVoice: A Speech Corpus and Fine-tuned Whisper ASR System for the Balti Language](https://mlsanity.com/paper/2606.03504/) (2026)

## Abstract (arXiv)

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.