5 citations · 6 across the 12 of their papers we have counts for
12 papers
CookVoice: Unified Framework for Style Controllable Multi-Modal Human Voice Generation
Haowei Lou, Hye-Young Paik, Dai Jia +2
Human voice generation has made rapid progress in speech generation, singing voice generation, voice cloning, and voice editing. However, most existing systems are designed for spe…
From Classification to Recommendation: Empirical Analysis of Audio Embedding Models Application for Content-Based Music Recommendation
Qingrui Li, Haowei Lou, Chengkai Huang +2
Pretrained audio representation models learned from large-scale corpora have achieved strong performance in audio classification and understanding. However, most existing models ar…
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…
Diffusion Models for Computational Neuroimaging: A Survey
Haokai Zhao, Haowei Lou, Lina Yao +4
Computational neuroimaging involves analyzing brain images or signals to provide mechanistic insights and predictive tools for human cognition and behavior. While diffusion models…
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…
Brain Network Diffusion-Driven fMRI Connectivity Augmentation for Enhanced Autism Spectrum Disorder Diagnosis
Haokai Zhao, Haowei Lou, Lina Yao +1
Functional magnetic resonance imaging (fMRI) is an emerging neuroimaging modality that is commonly modeled as networks of Regions of Interest (ROIs) and their connections, named fu…
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…