Speaker Style-Aware Phoneme Anchoring for Improved Cross-Lingual Speech Emotion Recognition
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| Format: | Preprint |
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2025
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| _version_ | 1866915511710777344 |
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| author | Upadhyay, Shreya G. Busso, Carlos Lee, Chi-Chun |
| author_facet | Upadhyay, Shreya G. Busso, Carlos Lee, Chi-Chun |
| contents | Cross-lingual speech emotion recognition (SER) remains a challenging task due to differences in phonetic variability and speaker-specific expressive styles across languages. Effectively capturing emotion under such diverse conditions requires a framework that can align the externalization of emotions across different speakers and languages. To address this problem, we propose a speaker-style aware phoneme anchoring framework that aligns emotional expression at the phonetic and speaker levels. Our method builds emotion-specific speaker communities via graph-based clustering to capture shared speaker traits. Using these groups, we apply dual-space anchoring in speaker and phonetic spaces to enable better emotion transfer across languages. Evaluations on the MSP-Podcast (English) and BIIC-Podcast (Taiwanese Mandarin) corpora demonstrate improved generalization over competitive baselines and provide valuable insights into the commonalities in cross-lingual emotion representation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_20373 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Speaker Style-Aware Phoneme Anchoring for Improved Cross-Lingual Speech Emotion Recognition Upadhyay, Shreya G. Busso, Carlos Lee, Chi-Chun Computation and Language Machine Learning Cross-lingual speech emotion recognition (SER) remains a challenging task due to differences in phonetic variability and speaker-specific expressive styles across languages. Effectively capturing emotion under such diverse conditions requires a framework that can align the externalization of emotions across different speakers and languages. To address this problem, we propose a speaker-style aware phoneme anchoring framework that aligns emotional expression at the phonetic and speaker levels. Our method builds emotion-specific speaker communities via graph-based clustering to capture shared speaker traits. Using these groups, we apply dual-space anchoring in speaker and phonetic spaces to enable better emotion transfer across languages. Evaluations on the MSP-Podcast (English) and BIIC-Podcast (Taiwanese Mandarin) corpora demonstrate improved generalization over competitive baselines and provide valuable insights into the commonalities in cross-lingual emotion representation. |
| title | Speaker Style-Aware Phoneme Anchoring for Improved Cross-Lingual Speech Emotion Recognition |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2509.20373 |