Multi-scale modulation filtering in automatic detection of emotions in telephone speech
Jouni Pohjalainen, Paavo Alku · 2014
This study investigates emotion detection from noise-corrupted telephone speech. A generic modulation filtering approach for audio pattern recognition is proposed that utilizes inherent long-term properties of acoustic features in different classes. When applied to binary classification along the activation and valence dimensions, filtering the baseline short-time timbral features in both the training and detection phase leads to significant improvement especially in noise robustness. Automatic selection of training data based on the filter's prediction residual further improves the results.