Vocal Feature

Vocal feature research focuses on extracting and analyzing acoustic properties of speech to understand various aspects of vocal production and perception. Current research employs machine learning techniques, including neural embeddings (like x-vectors and wav2vec 2.0) and dimensionality reduction methods (such as UMAP), to represent and classify vocal features for applications such as emotion recognition, fatigue detection, and speech synthesis. These advancements are improving the accuracy of automated speech analysis and impacting fields like human-computer interaction, healthcare (detecting vocal pathologies), and assistive technologies. The integration of vocal features with other modalities, such as text, is also a growing area of interest.