In this paper, a data augmentation method is proposed for depression detection from speech
signals. Samples for data augmentation were created by changing the frame-width and the …

While speech-based depression detection methods that use speaker-identity features, such
as speaker embeddings, are popular, they often compromise patient privacy. To address this …

Speech signals are valuable biomarkers for assessing an individual's mental health,
including identifying Major Depressive Disorder (MDD) automatically. A frequently used …

Speech-based degree of sleepiness estimation is an emerging research problem. This
paper investigates an end-to-end approach, where given raw waveform as input, a …

Abstract The Continuous Sleepiness detection task was a Sub-Challenge developed in the
2019 INTERSPEECH Computational Paralinguistics Challenge (ComParE). The associated …

Sleepiness is a major public and personal health issue. Measuring sleepiness in patients'
everyday living conditions would represent a significant advancement in managing them …

We propose an approach to extract speaker embeddings that are robust to speaking style
variations in text-independent speaker verification. Typically, speaker embedding extraction …

Our prior experiments show that humans and machines seem to employ different
approaches to speaker discrimination, especially in the presence of speaking style …

A speaker's voice constantly varies in everyday situations, such as when talking to a friend,
reading aloud, talking to pets, or narrating a happy incident. These changes in speaking …

Excessive sleepiness in critical tasks and jobs can lead to adverse outcomes, such as work
accidents and car crashes. Detecting and monitoring sleepiness levels can prevent these …