4 accepted papers
Respiratory audio, such as coughing and breathing sounds, has predictive power for a wide range of healthcare applications, yet is currently under-explored. The main problem for those applications arises from the difficulty in collecting large labeled task-specific data for model development. Genera…
Asthma affects an estimated 334 million people worldwide, causing over 461 000 deaths. Exacerbations or asthma attacks can be predicted with new sensor technologies. We explore how recordings of human voice, and machine learning can provide better diagnostics for pulmonary diseases like asthma, as w…
In this paper, we propose an adversarial network implementation for speech emotion conversion as a data augmentation method, validated by a multi-class speech affect recognition task. In our setting, we do not assume the availability of parallel data, and we additionally make it a priority to exploi…
Using data with high quality annotation is crucial in emotion recognition applications, especially because the task is subjective and the raters may exhibit disagreement with respect to each sample. In this paper, we propose a meta-learning methodology that can reason about the training data and det…