Towards Universal End-to-End Affect Recognition from Multilingual Speech by ConvNets
This work addresses the problem of developing universal affect recognition systems for applications like emotion and personality detection from speech, though it appears incremental as it builds on existing CNN architectures.
The authors tackled affect recognition from multilingual speech by proposing an end-to-end CNN that processes raw waveforms across multiple languages simultaneously, achieving average improvements of 12.8% for emotion and 10.1% for personality recognition compared to single-language training.
We propose an end-to-end affect recognition approach using a Convolutional Neural Network (CNN) that handles multiple languages, with applications to emotion and personality recognition from speech. We lay the foundation of a universal model that is trained on multiple languages at once. As affect is shared across all languages, we are able to leverage shared information between languages and improve the overall performance for each one. We obtained an average improvement of 12.8% on emotion and 10.1% on personality when compared with the same model trained on each language only. It is end-to-end because we directly take narrow-band raw waveforms as input. This allows us to accept as input audio recorded from any source and to avoid the overhead and information loss of feature extraction. It outperforms a similar CNN using spectrograms as input by 12.8% for emotion and 6.3% for personality, based on F-scores. Analysis of the network parameters and layers activation shows that the network learns and extracts significant features in the first layer, in particular pitch, energy and contour variations. Subsequent convolutional layers instead capture language-specific representations through the analysis of supra-segmental features. Our model represents an important step for the development of a fully universal affect recognizer, able to recognize additional descriptors, such as stress, and for the future implementation into affective interactive systems.