Large Language Models Meet Contrastive Learning: Zero-Shot Emotion Recognition Across Languages

• URL: http://arxiv.org/abs/2503.21806v1
• Date: Tue, 25 Mar 2025 05:58:18 GMT
• Title: Large Language Models Meet Contrastive Learning: Zero-Shot Emotion Recognition Across Languages
• Authors: Heqing Zou, Fengmao Lv, Desheng Zheng, Eng Siong Chng, Deepu Rajan,
• Abstract summary: We propose leveraging contrastive learning to refine multilingual speech features and extend large language models.<n>Specifically, we employ a novel two-stage training framework to align speech signals with linguistic features in the emotional space.<n>To advance research in this field, we introduce a large-scale synthetic multilingual speech emotion dataset, M5SER.
• Score: 31.15696076055884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multilingual speech emotion recognition aims to estimate a speaker's emotional state using a contactless method across different languages. However, variability in voice characteristics and linguistic diversity poses significant challenges for zero-shot speech emotion recognition, especially with multilingual datasets. In this paper, we propose leveraging contrastive learning to refine multilingual speech features and extend large language models for zero-shot multilingual speech emotion estimation. Specifically, we employ a novel two-stage training framework to align speech signals with linguistic features in the emotional space, capturing both emotion-aware and language-agnostic speech representations. To advance research in this field, we introduce a large-scale synthetic multilingual speech emotion dataset, M5SER. Our experiments demonstrate the effectiveness of the proposed method in both speech emotion recognition and zero-shot multilingual speech emotion recognition, including previously unseen datasets and languages.

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