arXiv:2606.07309v1 Announce Type: cross Abstract: Instruction-following audio language models (ALMs) can be augmented with explicit acoustic cues, yet it remains unclear whether such cues are used in a grounded way when the raw audio is already available. We study this question in speech emotion recognition (SER) by deriving six interpretable acoustic concept tokens from the standardised eGeMAPS paralinguistic feature set. These tokens summarise energy, pitch, dynamics, brightness, formants, and voice quality, and are appended to the textual prompt while the audio input is kept unchanged. Acro
The rapid advancement in AI, particularly large language models, is driving research into integrating various modalities like audio to enhance their capabilities and address real-world applications such as emotional intelligence.
This research signifies a step towards more capable and context-aware AI, enabling machines to understand and respond to human emotions, which is critical for natural human-computer interaction and various applications.
The ability to explicitly align acoustic cues with language models through interpretable tokens could lead to more robust and explainable emotion recognition, moving beyond black-box approaches.
- · AI developers
- · Customer service industries
- · Mental health applications
- · Human-computer interaction researchers
- · Platforms with limited audio processing capabilities
- · Basic sentiment analysis providers
Improved accuracy and explainability in speech emotion recognition within AI systems.
Development of more emotionally intelligent AI agents in applications like virtual assistants and therapeutic tools.
Ethical and privacy concerns around pervasive emotional surveillance and manipulation by advanced AI.
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Read at arXiv cs.AI