How does the integration of phonological and semantic embeddings in OpenPangu-MLA affect its zero-shot emotion

Abstract

Abstract: Speech inherently contains rich acoustic information that extends far beyond the textual language. In real-world spoken language understanding, effective interpretation often requires integrating semantic meaning (e.g., content), paralinguistic features (e.g., emotions, speed, pitch) and phonological characteristics (e.g., prosody, intonation, rhythm), which are embedded in speech. While recent multimodal Speech Large Language Models (SpeechLLMs) have demonstrated remarkable capabilities in processing audio information, their ability to perform fine-grained perception and complex reasoning in

Research Question

How does the integration of phonological and semantic embeddings in OpenPangu-MLA affect its zero-shot emotion recognition accuracy on the MMSU benchmark compared to prosody-only baselines?

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