39 citations · 99 across the 29 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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.…