From Statistical Methods to Pre-Trained Models; A Survey on Automatic Speech Recognition for Resource Scarce Urdu Language

TL;DR

This survey reviews recent advancements in automatic speech recognition for resource-scarce Urdu, highlighting challenges, datasets, algorithms, and future research directions to improve Urdu language processing.

Contribution

It provides a comprehensive overview of current Urdu ASR research, emphasizing technological trends, datasets, and potential avenues for future exploration.

Findings

Analysis of existing datasets and tools for Urdu ASR

Identification of key challenges in resource-scarce language processing

Suggestions for future research directions in Urdu speech recognition

Abstract

Automatic Speech Recognition (ASR) technology has witnessed significant advancements in recent years, revolutionizing human-computer interactions. While major languages have benefited from these developments, lesser-resourced languages like Urdu face unique challenges. This paper provides an extensive exploration of the dynamic landscape of ASR research, focusing particularly on the resource-constrained Urdu language, which is widely spoken across South Asian nations. It outlines current research trends, technological advancements, and potential directions for future studies in Urdu ASR, aiming to pave the way for forthcoming researchers interested in this domain. By leveraging contemporary technologies, analyzing existing datasets, and evaluating effective algorithms and tools, the paper seeks to shed light on the unique challenges and opportunities associated with Urdu language processing and its integration into the broader field of speech research.