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Unsupervised domain adaptation offers significant potential for cross-lingual speech emotion recognition (SER). Most relevant studies have addressed this problem as a domain mismatch without considering phonetical emotional differences across languages. Our study explores universal discrete speech units obtained with vector quantization of wavLM representations from emotional speech in English, Taiwanese Mandarin, and Russian. We estimate cluster-wise distributions of quantized wavLM frames to quantify phonetic commonalities and differences across languages, vowels, and emotions. Our findings indicate that certain emotion-specific phonemes exhibit cross-linguistic similarities. The distribution of vowels varies with emotional content. Certain vowels across languages show close distributional proximity, offering anchor points for cross-lingual domain adaptation. We also propose and validate a method to quantify phoneme distribution similarities across languages.
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This content will become publicly available on August 17, 2026
Vector Quantized Cross-lingual Unsupervised Domain Adaptation for Speech Emotion Recognition
Building speech emotion recognition (SER) models for low-resource languages is challenging due to the scarcity of labeled speech data. This limitation mandates the development of cross-lingual unsupervised domain adaptation techniques to effectively utilize labeled data from resource-rich languages. Inspired by the TransVQA framework, we propose a method that leverages a shared quantized feature space to enable knowledge transfer between labeled and unlabeled data across languages. The approach utilizes a quantized codebook to capture shared features, while reducing the domain gap, and aligning class distributions, thereby improving classification accuracy. Additionally, an information loss (InfoLoss) mechanism mitigates critical information loss during quantization. InfoLoss achieves this goal by minimizing the loss within the simplex of posterior class label distributions. The proposed method demonstrates superior performance compared to state-of-the-art baseline approaches. Index Terms: Speech Emotion Recognition, Cross-lingual Unsupervised Domain Adaptation, Discrete Features, InfoLoss
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- Award ID(s):
- 2016719
- PAR ID:
- 10655474
- Publisher / Repository:
- ISCA
- Date Published:
- Page Range / eLocation ID:
- 126 to 130
- Format(s):
- Medium: X
- Location:
- Rotterdam, The Netherlands
- Sponsoring Org:
- National Science Foundation
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