5 papers
SimSD: Simple Speculative Decoding in Diffusion Language Models
Junxia Cui, Haotian Ye, Runchu Tian +9
Diffusion large language models (dLLMs) have recently emerged as a promising alternative to autoregressive (AR) LLMs, offering faster inference through parallel or blockwise decodi…
Multi-Step Prediction and Control of Hierarchical Emotion Distribution in Text-to-Speech Synthesis
Sho Inoue, Kun Zhou, Shuai Wang +1
We investigate hierarchical emotion distribution (ED) for achieving multi-level quantitative control of emotion rendering in text-to-speech synthesis (TTS). We introduce a novel mu…
Hierarchical Control of Emotion Rendering in Speech Synthesis
Sho Inoue, Kun Zhou, Shuai Wang +1
Emotional text-to-speech synthesis (TTS) aims to generate realistic emotional speech from input text. However, quantitatively controlling multi-level emotion rendering remains chal…
Enhancing Emotional Text-to-Speech Controllability with Natural Language Guidance through Contrastive Learning and Diffusion Models
Xin Jing, Kun Zhou, Andreas Triantafyllopoulos +1
While current emotional text-to-speech (TTS) systems can generate highly intelligible emotional speech, achieving fine control over emotion rendering of the output speech still rem…
Emotional Dimension Control in Language Model-Based Text-to-Speech: Spanning a Broad Spectrum of Human Emotions
Kun Zhou, You Zhang, Dianwen Ng +3
Emotional text-to-speech (TTS) systems sturggle to capture the full spectrum of human emotions due to the inherent complexity of emotional expressions and the limited coverage of e…