5 citations · 6 across the 9 of their papers we have counts for
9 papers
AutoSIFT: Automatic Style Sifting for Controllable Speech Generation with Arbitrary Style Infilling
Haowei Lou, Junda Wu, Chengkai Huang +4
State-of-the-art text-to-speech (TTS) models achieve impressive naturalness and expressiveness, yet fine-grained, disentangled control over speaking styles remains challenging. In…
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…
LatentSpeech: Latent Diffusion for Text-To-Speech Generation
Haowei Lou, Helen Paik, Pari Delir Haghighi +2
Diffusion-based Generative AI gains significant attention for its superior performance over other generative techniques like Generative Adversarial Networks and Variational Autoenc…
Aligner-Guided Training Paradigm: Advancing Text-to-Speech Models with Aligner Guided Duration
Haowei Lou, Helen Paik, Wen Hu +1
Recent advancements in text-to-speech (TTS) systems, such as FastSpeech and StyleSpeech, have significantly improved speech generation quality. However, these models often rely on…
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…
StyleSpeech: Parameter-efficient Fine Tuning for Pre-trained Controllable Text-to-Speech
Haowei Lou, Helen Paik, Wen Hu +1
This paper introduces StyleSpeech, a novel Text-to-Speech~(TTS) system that enhances the naturalness and accuracy of synthesized speech. Building upon existing TTS technologies, St…