Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis
This addresses the challenge of integrating underdeveloped audio and video features with advanced text features for more accurate multimodal language analysis, though it appears incremental as it builds on existing deep canonical correlation analysis.
The paper tackled the problem of multimodal sentiment analysis and emotion recognition by learning hidden correlations between text, audio, and video features, resulting in improved performance on benchmark datasets compared to other state-of-the-art methods.
Multimodal language analysis often considers relationships between features based on text and those based on acoustical and visual properties. Text features typically outperform non-text features in sentiment analysis or emotion recognition tasks in part because the text features are derived from advanced language models or word embeddings trained on massive data sources while audio and video features are human-engineered and comparatively underdeveloped. Given that the text, audio, and video are describing the same utterance in different ways, we hypothesize that the multimodal sentiment analysis and emotion recognition can be improved by learning (hidden) correlations between features extracted from the outer product of text and audio (we call this text-based audio) and analogous text-based video. This paper proposes a novel model, the Interaction Canonical Correlation Network (ICCN), to learn such multimodal embeddings. ICCN learns correlations between all three modes via deep canonical correlation analysis (DCCA) and the proposed embeddings are then tested on several benchmark datasets and against other state-of-the-art multimodal embedding algorithms. Empirical results and ablation studies confirm the effectiveness of ICCN in capturing useful information from all three views.