755 citations · 2.7k across the 91 of their papers we have counts for
95 papers
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…
Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model
Yuezhou Zhang, Amos A Folarin, Judith Dineley +25
Language use has been shown to correlate with depression, but large-scale validation is needed. Traditional methods like clinic studies are expensive. So, natural language processi…
Bringing the Discussion of Minima Sharpness to the Audio Domain: a Filter-Normalised Evaluation for Acoustic Scene Classification
Manuel Milling, Andreas Triantafyllopoulos, Iosif Tsangko +2
The correlation between the sharpness of loss minima and generalisation in the context of deep neural networks has been subject to discussion for a long time. Whilst mostly investi…
Exploring Meta Information for Audio-based Zero-shot Bird Classification
Alexander Gebhard, Andreas Triantafyllopoulos, Teresa Bez +3
Advances in passive acoustic monitoring and machine learning have led to the procurement of vast datasets for computational bioacoustic research. Nevertheless, data scarcity is sti…
A Wide Evaluation of ChatGPT on Affective Computing Tasks
Mostafa M. Amin, Rui Mao, Erik Cambria +1
With the rise of foundation models, a new artificial intelligence paradigm has emerged, by simply using general purpose foundation models with prompting to solve problems instead o…
Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models
Zixing Zhang, Liyizhe Peng, Tao Pang +3
After the inception of emotion recognition or affective computing, it has increasingly become an active research topic due to its broad applications. Over the past couple of decade…
Can ChatGPT's Responses Boost Traditional Natural Language Processing?
Mostafa M. Amin, Erik Cambria, Björn W. Schuller
The employment of foundation models is steadily expanding, especially with the launch of ChatGPT and the release of other foundation models. These models have shown the potential o…
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…
Happy or Evil Laughter? Analysing a Database of Natural Audio Samples
Aljoscha Düsterhöft, Felix Burkhardt, Björn W. Schuller
We conducted a data collection on the basis of the Google AudioSet database by selecting a subset of the samples annotated with \textit{laughter}. The selection criterion was to be…
U-DiT TTS: U-Diffusion Vision Transformer for Text-to-Speech
Xin Jing, Yi Chang, Zijiang Yang +3
Deep learning has led to considerable advances in text-to-speech synthesis. Most recently, the adoption of Score-based Generative Models (SGMs), also known as Diffusion Probabilist…
The MuSe 2023 Multimodal Sentiment Analysis Challenge: Mimicked Emotions, Cross-Cultural Humour, and Personalisation
Lukas Christ, Shahin Amiriparian, Alice Baird +10
The MuSe 2023 is a set of shared tasks addressing three different contemporary multimodal affect and sentiment analysis problems: In the Mimicked Emotions Sub-Challenge (MuSe-Mimic…
The ACM Multimedia 2023 Computational Paralinguistics Challenge: Emotion Share & Requests
Björn W. Schuller, Anton Batliner, Shahin Amiriparian +10
The ACM Multimedia 2023 Computational Paralinguistics Challenge addresses two different problems for the first time in a research competition under well-defined conditions: In the…
MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning
Zheng Lian, Haiyang Sun, Licai Sun +15
The first Multimodal Emotion Recognition Challenge (MER 2023) was successfully held at ACM Multimedia. The challenge focuses on system robustness and consists of three distinct tra…
hierarchical network with decoupled knowledge distillation for speech emotion recognition
Ziping Zhao, Huan Wang, Haishuai Wang +1
The goal of Speech Emotion Recognition (SER) is to enable computers to recognize the emotion category of a given utterance in the same way that humans do. The accuracy of SER is st…
Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPT
Mostafa M. Amin, Erik Cambria, Björn W. Schuller
ChatGPT has shown the potential of emerging general artificial intelligence capabilities, as it has demonstrated competent performance across many natural language processing tasks…
audb -- Sharing and Versioning of Audio and Annotation Data in Python
Hagen Wierstorf, Johannes Wagner, Florian Eyben +2
Driven by the need for larger and more diverse datasets to pre-train and fine-tune increasingly complex machine learning models, the number of datasets is rapidly growing. audb is…
Automatic Emotion Modelling in Written Stories
Lukas Christ, Shahin Amiriparian, Manuel Milling +2
Telling stories is an integral part of human communication which can evoke emotions and influence the affective states of the audience. Automatically modelling emotional trajectori…
Fast Yet Effective Speech Emotion Recognition with Self-distillation
Zhao Ren, Thanh Tam Nguyen, Yi Chang +1
Speech emotion recognition (SER) is the task of recognising human's emotional states from speech. SER is extremely prevalent in helping dialogue systems to truly understand our emo…
Propagating Variational Model Uncertainty for Bioacoustic Call Label Smoothing
Georgios Rizos, Jenna Lawson, Simon Mitchell +5
We focus on using the predictive uncertainty signal calculated by Bayesian neural networks to guide learning in the self-same task the model is being trained on. Not opting for cos…
Audio Barlow Twins: Self-Supervised Audio Representation Learning
Jonah Anton, Harry Coppock, Pancham Shukla +1
The Barlow Twins self-supervised learning objective requires neither negative samples or asymmetric learning updates, achieving results on a par with the current state-of-the-art w…
Dynamic Restrained Uncertainty Weighting Loss for Multitask Learning of Vocal Expression
Meishu Song, Zijiang Yang, Andreas Triantafyllopoulos +6
We propose a novel Dynamic Restrained Uncertainty Weighting Loss to experimentally handle the problem of balancing the contributions of multiple tasks on the ICML ExVo 2022 Challen…
The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes
Björn W. Schuller, Anton Batliner, Shahin Amiriparian +12
The ACM Multimedia 2022 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the…
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…
An Overview & Analysis of Sequence-to-Sequence Emotional Voice Conversion
Zijiang Yang, Xin Jing, Andreas Triantafyllopoulos +3
Emotional voice conversion (EVC) focuses on converting a speech utterance from a source to a target emotion; it can thus be a key enabling technology for human-computer interaction…