Year: 2026
Type: conference-paper
Abstract: Neural speech codecs provide discrete representations for speech language models, but emotional cues are often degraded during quantization.Existing codecs mainly optimize acoustic reconstruction, leaving emotion expressiveness insufficiently modeled at the representation level.We propose an emotionguided neural speech codec that explicitly preserves emotional information while maintaining semantic fidelity and prosodic naturalness.Our framework combines emotion-semantic guided latent modulation, relation-preserving emotional-semantic distillation, and emotionweighted semantic alignment to retain emotionally salient cues under compression.Extensive evaluations across speech reconstruction, emotion recognition, and downstream text to speech generation demonstrate improved emotion consistency and perceptual quality without sacrificing content accuracy.
Institutions: Emory University, George Mason University, William & Mary
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166838889