Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion Recognition

no code implementations • 21 Jun 2022 • Einari Vaaras, Manu Airaksinen, Okko Räsänen

In this paper, we combine CPC and multiple dimensionality reduction methods in search of functioning practices for clustering-based AL. Our experiments for simulating speech emotion recognition system deployment show that both the local and global topology of the feature space can be successfully used for AL, and that CPC can be used to improve clustering-based AL performance over traditional signal features.

Active Learning Dimensionality Reduction +2

Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit

no code implementations • 14 Jun 2021 • Einari Vaaras, Sari Ahlqvist-Björkroth, Konstantinos Drossos, Okko Räsänen

Researchers have recently started to study how the emotional speech heard by young infants can affect their developmental outcomes.

Active Learning Cross-corpus +2