A powerful 3B-parameter, LLM-based Reinforcement Learning audio edit model excels at editing emotion, speaking style, and paralinguistics, and features robust zero-shot text-to-speech

Scripts for analyzing how the extent of coarticulation varies across different communicative contexts using speech samples from the LUCID corpus

Aligning latent space of speaking style with human perception using a re-embedding strategy