Beyond Isolated Utterances: Conversational Emotion Recognition
This addresses the problem of recognizing emotions in conversations for applications like human-computer interaction, but it is incremental as it builds on existing methods with specific enhancements.
The paper tackles conversational emotion recognition by treating it as a sequence labeling task, proposing transformer-based models and an augmentation scheme that improved performance by 3.3% micro-f1 on conversations and 3.6% on isolated utterances.
Speech emotion recognition is the task of recognizing the speaker's emotional state given a recording of their utterance. While most of the current approaches focus on inferring emotion from isolated utterances, we argue that this is not sufficient to achieve conversational emotion recognition (CER) which deals with recognizing emotions in conversations. In this work, we propose several approaches for CER by treating it as a sequence labeling task. We investigated transformer architecture for CER and, compared it with ResNet-34 and BiLSTM architectures in both contextual and context-less scenarios using IEMOCAP corpus. Based on the inner workings of the self-attention mechanism, we proposed DiverseCatAugment (DCA), an augmentation scheme, which improved the transformer model performance by an absolute 3.3% micro-f1 on conversations and 3.6% on isolated utterances. We further enhanced the performance by introducing an interlocutor-aware transformer model where we learn a dictionary of interlocutor index embeddings to exploit diarized conversations.