12 papers · 1 filter
Large language models for mental health
Andreas Triantafyllopoulos, Yannik Terhorst, Iosif Tsangko +10
Digital technologies have long been explored as a complement to standard procedure in mental health research and practice, ranging from the management of electronic health records…
Does the Definition of Difficulty Matter? Scoring Functions and their Role for Curriculum Learning
Simon Rampp, Manuel Milling, Andreas Triantafyllopoulos +1
Curriculum learning (CL) describes a machine learning training strategy in which samples are gradually introduced into the training process based on their difficulty. Despite a par…
Enhancing Emotional Text-to-Speech Controllability with Natural Language Guidance through Contrastive Learning and Diffusion Models
Xin Jing, Kun Zhou, Andreas Triantafyllopoulos +1
While current emotional text-to-speech (TTS) systems can generate highly intelligible emotional speech, achieving fine control over emotion rendering of the output speech still rem…
Audio Enhancement for Computer Audition -- An Iterative Training Paradigm Using Sample Importance
Manuel Milling, Shuo Liu, Andreas Triantafyllopoulos +2
Neural network models for audio tasks, such as automatic speech recognition (ASR) and acoustic scene classification (ASC), are susceptible to noise contamination for real-life appl…
Abusive Speech Detection in Indic Languages Using Acoustic Features
Anika A. Spiesberger, Andreas Triantafyllopoulos, Iosif Tsangko +1
Abusive content in online social networks is a well-known problem that can cause serious psychological harm and incite hatred. The ability to upload audio data increases the import…
ParaCLAP -- Towards a general language-audio model for computational paralinguistic tasks
Xin Jing, Andreas Triantafyllopoulos, Björn Schuller
Contrastive language-audio pretraining (CLAP) has recently emerged as a method for making audio analysis more generalisable. Specifically, CLAP-style models are able to `answer' a…