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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 

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. 

The Interspeech 2025 speech emotion recognition in natural istic conditions challenge builds on previous efforts to advance speech emotion recognition (SER) in real-world scenarios. The focus is on recognizing emotions from spontaneous speech, moving beyond controlled datasets. It provides a framework for speaker-independent training, development, and evaluation, with annotations for both categorical and dimensional tasks. The challenge attracted 93 research teams, whose models significantly improved state-of-the-art results over competitive baselines. This paper summarizes the challenge, focusing on the key outcomes. We analyze top-performing methods, emerging trends, and innovative directions. We highlight the effectiveness of combining foundational models based on audio and text to achieve robust SER systems. The competition website, with leaderboards, baseline code, and instructions, is available at: https://lab-msp.com/MSP-Podcast_Competition/IS2025/.