activity
20132023
most citedSecuring Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward

246 citations · 1.4k across the 71 of their papers we have counts for

collaborators

87 papers

cs.CL2023★ 8 cited

Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers

Syed Aun Muhammad Zaidi, Siddique Latif, Junaid Qadir

Despite the recent progress in speech emotion recognition (SER), state-of-the-art systems are unable to achieve improved performance in cross-language settings. In this paper, we p…

cs.RO2023★ 1 cited

Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and Techniques

Mohammed Aledhari, Mohamed Rahouti, Junaid Qadir +3

This article outlines the architecture of autonomous driving and related complementary frameworks from the perspective of human comfort. The technical elements for measuring Autono…

cs.SD2023★ 6 cited

Emotions Beyond Words: Non-Speech Audio Emotion Recognition With Edge Computing

Ibrahim Malik, Siddique Latif, Sanaullah Manzoor +3

Non-speech emotion recognition has a wide range of applications including healthcare, crime control and rescue, and entertainment, to name a few. Providing these applications using…

cs.CL2023★ 17 cited

Transformers in Speech Processing: A Survey

Siddique Latif, Aun Zaidi, Heriberto Cuayahuitl +4

The remarkable success of transformers in the field of natural language processing has sparked the interest of the speech-processing community, leading to an exploration of their p…

cs.SD2023★ 2 cited

Generative Emotional AI for Speech Emotion Recognition: The Case for Synthetic Emotional Speech Augmentation

Abdullah Shahid, Siddique Latif, Junaid Qadir

Despite advances in deep learning, current state-of-the-art speech emotion recognition (SER) systems still have poor performance due to a lack of speech emotion datasets. This pape…

cs.HC2022★ 13 cited

AI-Based Emotion Recognition: Promise, Peril, and Prescriptions for Prosocial Path

Siddique Latif, Hafiz Shehbaz Ali, Muhammad Usama +3

Automated emotion recognition (AER) technology can detect humans' emotional states in real-time using facial expressions, voice attributes, text, body movements, and neurological s…