Attention Driven Fusion for Multi-Modal Emotion Recognition

TL;DR

This paper introduces a deep learning approach that fuses acoustic and text data using attention mechanisms and specialized feature extraction layers to improve emotion recognition accuracy on the IEMOCAP dataset.

Contribution

It proposes a novel multi-modal fusion method with SincNet for acoustic features and cross attention for text, enhancing emotion classification performance.

Findings

Achieved 3.5% improvement in weighted accuracy over state-of-the-art methods.

Utilized SincNet for more effective acoustic feature extraction.

Introduced cross attention to model N-gram level correlations in text.

Abstract

Deep learning has emerged as a powerful alternative to hand-crafted methods for emotion recognition on combined acoustic and text modalities. Baseline systems model emotion information in text and acoustic modes independently using Deep Convolutional Neural Networks (DCNN) and Recurrent Neural Networks (RNN), followed by applying attention, fusion, and classification. In this paper, we present a deep learning-based approach to exploit and fuse text and acoustic data for emotion classification. We utilize a SincNet layer, based on parameterized sinc functions with band-pass filters, to extract acoustic features from raw audio followed by a DCNN. This approach learns filter banks tuned for emotion recognition and provides more effective features compared to directly applying convolutions over the raw speech signal. For text processing, we use two branches (a DCNN and a Bi-direction RNN followed by a DCNN) in parallel where cross attention is introduced to infer the N-gram level correlations on hidden representations received from the Bi-RNN. Following existing state-of-the-art, we evaluate the performance of the proposed system on the IEMOCAP dataset. Experimental results indicate that the proposed system outperforms existing methods, achieving 3.5% improvement in weighted accuracy.