Cross lingual speech emotion recognition using canonical correlation analysis on principal component subspace

Hesam Sagha; Jun Deng; Maryna Gavryukova; Jing Han; Björn W. Schuller

ICASSP 2016accepted0 citations

Cross lingual speech emotion recognition using canonical correlation analysis on principal component subspace

Hesam Sagha, Jun Deng, Maryna Gavryukova, Jing Han, Björn W. Schuller

Abstract

This paper proposes an analytical approach based on Kernel Canonical Correlation Analysis (KCCA) for domain adaptation. To generate paired instances for KCCA, we mapped source and target data onto both source and target principal components. We performed pair-wise domain adaptation between four emotional speech corpora with different languages (English, German, Italian, and Polish) to validate the approach. We compared our approach with the Shared-Hidden-Layer Auto-Encoder (SHLA) and kernel based principal components. On average, the proposed approach yields higher classification performance.

BibTeX

@inproceedings{icassp2016_crosslingualspee,
  title = {Cross lingual speech emotion recognition using canonical correlation analysis on principal component subspace},
  author = {Hesam Sagha and Jun Deng and Maryna Gavryukova and Jing Han and Björn W. Schuller},
  booktitle = {ICASSP 2016},
  year = {2016}
}