EMODE: Dynamic Para-Semantic Experts for Emotion-Aware Speech Language Modeling
cs.CL, cs.MM, cs.SD
Submitted: 2026-10-03
Updated: 2026-10-03
Sources
- Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs
- The MSP-Podcast Corpus
- Qwen2-Audio Technical Report
- Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
- OSUM-EChat: Enhancing End-to-End Empathetic Spoken Chatbot via Understanding-Driven Spoken Dialogue
- Distilling an End-to-End Voice Assistant Without Instruction Training Data
- Kimi-Audio Technical Report
- Megrez-Omni Technical Report
- BLSP: Bootstrapping Language-Speech Pre-training via Behavior Alignment of Continuation Writing
- Qwen2.5-Omni Technical Report
- GLM-4-Voice: Towards Intelligent and Human-Like End-to-End Spoken Chatbot
Related papers
- Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
- Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
- Subliminal Steering: Stronger Encoding of Hidden Signals
- MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
- The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
- Untangling the Mechanisms of Misleading Context in Medical Question Answering