Qieemo: Speech Is All You Need in the Emotion Recognition in Conversations

• URL: http://arxiv.org/abs/2503.22687v1
• Date: Wed, 05 Mar 2025 07:02:30 GMT
• Title: Qieemo: Speech Is All You Need in the Emotion Recognition in Conversations
• Authors: Jinming Chen, Jingyi Fang, Yuanzhong Zheng, Yaoxuan Wang, Haojun Fei,
• Abstract summary: Multimodal approaches benefit from the fusion of diverse modalities, thereby improving the recognition accuracy.<n>The proposed Qieemo framework effectively utilizes the pretrained automatic speech recognition (ASR) model which contains naturally frame aligned textual and emotional features.<n>The experimental results on the IEMOCAP dataset demonstrate that Qieemo outperforms the benchmark unimodal, multimodal, and self-supervised models with absolute improvements of 3.0%, 1.2%, and 1.9% respectively.
• Score: 1.0690007351232649
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Emotion recognition plays a pivotal role in intelligent human-machine interaction systems. Multimodal approaches benefit from the fusion of diverse modalities, thereby improving the recognition accuracy. However, the lack of high-quality multimodal data and the challenge of achieving optimal alignment between different modalities significantly limit the potential for improvement in multimodal approaches. In this paper, the proposed Qieemo framework effectively utilizes the pretrained automatic speech recognition (ASR) model backbone which contains naturally frame aligned textual and emotional features, to achieve precise emotion classification solely based on the audio modality. Furthermore, we design the multimodal fusion (MMF) module and cross-modal attention (CMA) module in order to fuse the phonetic posteriorgram (PPG) and emotional features extracted by the ASR encoder for improving recognition accuracy. The experimental results on the IEMOCAP dataset demonstrate that Qieemo outperforms the benchmark unimodal, multimodal, and self-supervised models with absolute improvements of 3.0%, 1.2%, and 1.9% respectively.

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