arXiv:2606.27717v1 Announce Type: cross Abstract: Prosodic emphasis varies across languages, emotions, and speaking styles, yet existing emphasis detection models are largely trained and evaluated on monolingual neutral read speech. We introduce MMEE (Multilingual Multi-Emotion Emphasis), a corpus of 10,000 professionally recorded expressive utterances (14.13 hours) across 7 languages and 34 emotion/style categories, with three-level perceptual labels (10 annotations per sample). We benchmark two state-of-the-art architectures under monolingual, cross-lingual, multilingual, cross-emotion, cros
The paper addresses a critical generalization gap in speech AI, building multilingual, multi-emotional datasets to improve practical model performance at a time when AI language capabilities are rapidly advancing.
This research is crucial for developing robust and globally applicable AI systems capable of understanding and generating speech with appropriate prosodic emphasis across diverse languages and emotional contexts, impacting human-computer interaction and content creation.
The introduction of MMEE, a multilingual and multi-emotion corpus, significantly advances the state-of-the-art in speech emphasis modeling by enabling training and evaluation on far more diverse linguistic and emotional data.
- · AI researchers
- · Speech technology companies
- · Multilingual content creators
- · Users of voice AI interfaces
- · Monolingual speech AI models
- · Datasets lacking prosodic and emotional diversity
Improved accuracy and naturalness of speech synthesis and recognition across various languages and emotional states.
Accelerated development of more empathetic and effective AI assistants and virtual characters that can better understand and convey human nuance.
Potential for new applications in mental health support, education, and entertainment due to more sophisticated emotional understanding by AI.
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