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Learning Emotion-discriminative Representations for Zero-Shot Cross-lingual Speech Emotion Recognition

2026-06-04 14:05:38
Jinyi Mi, Ding Ma, Tomoki Toda
arXiv_SD
                 
Abstract

Zero-shot cross-lingual speech emotion recognition (SER) remains challenging due to distribution mismatches across languages and the lack of emotion annotations in target language. Under such conditions, models trained solely on source-language data frequently suffer from degraded generalization when evaluated on unseen target languages. To address this limitation, we propose an emotion-discriminative representation learning method that integrates supervised contrastive learning and speaker adversarial learning. The contrastive learning promotes cross-lingual emotion alignment, while speaker adversarial learning suppresses speaker-related cues to encourage speaker-invariant representations. Experimental results under a zero-shot cross-lingual SER setting demonstrate that the proposed method significantly improves SER performance over conventional training strategies.

Abstract (translated)

URL

https://arxiv.org/abs/2606.06200

PDF

https://arxiv.org/pdf/2606.06200.pdf

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