Urdu Speech Emotion Recognition: A Systematic Literature Review-Reference-Cited by-同舟云学术

Urdu Speech Emotion Recognition: A Systematic Literature Review

Published:2023-07-20 Issue:7 Volume:22 Page:1-33
ISSN:2375-4699
Container-title:ACM Transactions on Asian and Low-Resource Language Information Processing
language:en
Short-container-title:ACM Trans. Asian Low-Resour. Lang. Inf. Process.

Author:

Taj Soonh¹^ORCID,Mujtaba Ghulam¹^ORCID,Daudpota Sher Muhammad¹^ORCID,Mughal Muhammad Hussain¹^ORCID

Affiliation:

1. Department of Computer Science, Sukkur IBA University

Abstract

Research on Speech Emotion Recognition is becoming more mature day by day, and a lot of research is being carried out on Speech Emotion Recognition in resource-rich languages like English, German, French, and Chinese. Urdu is among the top 10 languages spoken worldwide. Despite its importance, few studies have worked on Urdu Speech emotion as Urdu is recognized as a resource-poor language. The Urdu language lacks publicly available datasets, and for this reason, few researchers have worked on Urdu Speech Emotion Recognition. To the best of our knowledge, no review has been found on Urdu Speech Emotion recognition. This study is the first systematic literature review on Urdu Speech Emotion Recognition, and the primary goal of this study is to provide a detailed analysis of the literature on Urdu Speech Emotion Recognition which includes the datasets, features, pre-processing, approaches, performance metrics, and validation methods used for Urdu Speech Emotion Recognition. This study also highlights the challenges and future directions for Urdu Speech Emotion Recognition.

Funder

National Research Program for Universities

Higher Education Commission

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Link

https://dl.acm.org/doi/pdf/10.1145/3595377

Reference77 articles.

1. Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, and Michael Isard. 2016. TensorFlow: A System for Large-Scale Machine Learning. In Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation (Savannah, GA, USA) (OSDI'16). USENIX Association, 265–283.

2. Deep learning techniques for speech emotion recognition, from databases to models;Joze Abbaschian Babak;Sensors,2021

3. Noise reduction of speech signal using wavelet transform with modified universal threshold;Aggarwal Rajeev;International Journal of Computer Applications,2011

4. Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers;Berkehan Akçay Mehmet;Speech Communication,2020

5. Emotion recognition in never-seen languages using a novel ensemble method with emotion profiles;Marcelo Albornoz Enrique;IEEE Transactions on Affective Computing,2015