From the 1 of 9 linked papers with an AI index.
9 papers
AutoSIFT: Automatic Style Sifting for Controllable Speech Generation with Arbitrary Style Infilling
Haowei Lou, Junda Wu, Chengkai Huang +4
AutoSIFT is a text-to-speech framework that separates speaking style into explicit categories (e.g., emotion, age) and residual prosodic details, allowing users to edit specific st…
ParaMETA: Towards Learning Disentangled Paralinguistic Speaking Styles Representations from Speech
Haowei Lou, Hye-young Paik, Wen Hu +1
Learning representative embeddings for different types of speaking styles, such as emotion, age, and gender, is critical for both recognition tasks (e.g., cognitive computing and h…
SpeechAgent: An End-to-End Mobile Infrastructure for Speech Impairment Assistance
Haowei Lou, Chengkai Huang, Hye-young Paik +4
Speech is essential for human communication, yet millions of people face impairments such as dysarthria, stuttering, and aphasia conditions that often lead to social isolation and…
ParaStyleTTS: Toward Efficient and Robust Paralinguistic Style Control for Expressive Text-to-Speech Generation
Haowei Lou, Hye-Young Paik, Wen Hu +1
Controlling speaking style in text-to-speech (TTS) systems has become a growing focus in both academia and industry. While many existing approaches rely on reference audio to guide…
Generalized Multilingual Text-to-Speech Generation with Language-Aware Style Adaptation
Haowei Lou, Hye-young Paik, Sheng Li +2
Text-to-Speech (TTS) models can generate natural, human-like speech across multiple languages by transforming phonemes into waveforms. However, multilingual TTS remains challenging…
Dual Conditional Diffusion Models for Sequential Recommendation
Hongtao Huang, Chengkai Huang, Tong Yu +4
Recent advancements in diffusion models have shown promising results in sequential recommendation (SR). Existing approaches predominantly rely on implicit conditional diffusion mod…