87 citations · 387 across the 34 of their papers we have counts for
40 papers
Generative AI in Signal Processing Education: An Audio Foundation Model Based Approach
Muhammad Salman Khan, Ahmad Ullah, Siddique Latif +1
Audio Foundation Models (AFMs), a specialized category of Generative AI (GenAI), have the potential to transform signal processing (SP) education by integrating core applications s…
Sparks of Large Audio Models: A Survey and Outlook
Siddique Latif, Moazzam Shoukat, Fahad Shamshad +8
This survey paper provides a comprehensive overview of the recent advancements and challenges in applying large language models to the field of audio signal processing. Audio proce…
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…
Can Large Language Models Aid in Annotating Speech Emotional Data? Uncovering New Frontiers
Siddique Latif, Muhammad Usama, Mohammad Ibrahim Malik +1
Despite recent advancements in speech emotion recognition (SER) models, state-of-the-art deep learning (DL) approaches face the challenge of the limited availability of annotated d…
A Preliminary Study on Augmenting Speech Emotion Recognition using a Diffusion Model
Ibrahim Malik, Siddique Latif, Raja Jurdak +1
In this paper, we propose to utilise diffusion models for data augmentation in speech emotion recognition (SER). In particular, we present an effective approach to utilise improved…
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…
Lightweight Toxicity Detection in Spoken Language: A Transformer-based Approach for Edge Devices
Ahlam Husni Abu Nada, Siddique Latif, Junaid Qadir
Toxicity is a prevalent social behavior that involves the use of hate speech, offensive language, bullying, and abusive speech. While text-based approaches for toxicity detection a…
MESAHA-Net: Multi-Encoders based Self-Adaptive Hard Attention Network with Maximum Intensity Projections for Lung Nodule Segmentation in CT Scan
Muhammad Usman, Azka Rehman, Abd Ur Rehman +5
Accurate lung nodule segmentation is crucial for early-stage lung cancer diagnosis, as it can substantially enhance patient survival rates. Computed tomography (CT) images are wide…
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…
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…
MEDS-Net: Self-Distilled Multi-Encoders Network with Bi-Direction Maximum Intensity projections for Lung Nodule Detection
Muhammad Usman, Azka Rehman, Abdullah Shahid +6
In this study, we propose a lung nodule detection scheme which fully incorporates the clinic workflow of radiologists. Particularly, we exploit Bi-Directional Maximum intensity pro…
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…
Multitask Learning from Augmented Auxiliary Data for Improving Speech Emotion Recognition
Siddique Latif, Rajib Rana, Sara Khalifa +2
Despite the recent progress in speech emotion recognition (SER), state-of-the-art systems lack generalisation across different conditions. A key underlying reason for poor generali…
Self Supervised Adversarial Domain Adaptation for Cross-Corpus and Cross-Language Speech Emotion Recognition
Siddique Latif, Rajib Rana, Sara Khalifa +2
Despite the recent advancement in speech emotion recognition (SER) within a single corpus setting, the performance of these SER systems degrades significantly for cross-corpus and…
Privacy Enhanced Speech Emotion Communication using Deep Learning Aided Edge Computing
Hafiz Shehbaz Ali, Fakhar ul Hassan, Siddique Latif +2
Speech emotion sensing in communication networks has a wide range of applications in real life. In these applications, voice data are transmitted from the user to the central serve…
A Survey on Deep Reinforcement Learning for Audio-Based Applications
Siddique Latif, Heriberto Cuayáhuitl, Farrukh Pervez +3
Deep reinforcement learning (DRL) is poised to revolutionise the field of artificial intelligence (AI) by endowing autonomous systems with high levels of understanding of the real…
Deep Reinforcement Learning with Pre-training for Time-efficient Training of Automatic Speech Recognition
Thejan Rajapakshe, Siddique Latif, Rajib Rana +2
Deep reinforcement learning (deep RL) is a combination of deep learning with reinforcement learning principles to create efficient methods that can learn by interacting with its en…
Deep Architecture Enhancing Robustness to Noise, Adversarial Attacks, and Cross-corpus Setting for Speech Emotion Recognition
Siddique Latif, Rajib Rana, Sara Khalifa +2
Speech emotion recognition systems (SER) can achieve high accuracy when the training and test data are identically distributed, but this assumption is frequently violated in practi…
Augmenting Generative Adversarial Networks for Speech Emotion Recognition
Siddique Latif, Muhammad Asim, Rajib Rana +3
Generative adversarial networks (GANs) have shown potential in learning emotional attributes and generating new data samples. However, their performance is usually hindered by the…
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends
Siddique Latif, Rajib Rana, Sara Khalifa +3
Research on speech processing has traditionally considered the task of designing hand-engineered acoustic features (feature engineering) as a separate distinct problem from the tas…
Pre-training in Deep Reinforcement Learning for Automatic Speech Recognition
Thejan Rajapakshe, Rajib Rana, Siddique Latif +2
Deep reinforcement learning (deep RL) is a combination of deep learning with reinforcement learning principles to create efficient methods that can learn by interacting with its en…
Unsupervised Adversarial Domain Adaptation for Cross-Lingual Speech Emotion Recognition
Siddique Latif, Junaid Qadir, Muhammad Bilal
Cross-lingual speech emotion recognition (SER) is a crucial task for many real-world applications. The performance of SER systems is often degraded by the differences in the distri…
Multi-Task Semi-Supervised Adversarial Autoencoding for Speech Emotion Recognition
Siddique Latif, Rajib Rana, Sara Khalifa +3
Inspite the emerging importance of Speech Emotion Recognition (SER), the state-of-the-art accuracy is quite low and needs improvement to make commercial applications of SER viable.…
Caveat emptor: the risks of using big data for human development
Siddique Latif, Adnan Qayyum, Muhammad Usama +3
Big data revolution promises to be instrumental in facilitating sustainable development in many sectors of life such as education, health, agriculture, and in combating humanitaria…