A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf
.
Locality Preserved Joint Nonnegative Matrix Factorization for Speech Emotion Recognition
2019
IEICE transactions on information and systems
This study presents a joint dictionary learning approach for speech emotion recognition named locality preserved joint nonnegative matrix factorization (LP-JNMF). The learned representations are shared between the learned dictionaries and annotation matrix. Moreover, a locality penalty term is incorporated into the objective function. Thus, the system's discriminability is further improved.
doi:10.1587/transinf.2018dal0002
fatcat:3atsfbqtenaunh5tawcr4sdx74