most citedMuLanTTS: The Microsoft Speech Synthesis System for Blizzard Challenge 2023

1 citations · 2 across the 6 of their papers we have counts for

collaborators

7 papers

cs.SD2025

Efficient Speech Watermarking for Speech Synthesis via Progressive Knowledge Distillation

Yang Cui, Peter Pan, Lei He +1

With the rapid advancement of speech generative models, unauthorized voice cloning poses significant privacy and security risks. Speech watermarking offers a viable solution for tr…

cs.CL2025

Fine-Tuning Large Multimodal Models for Automatic Pronunciation Assessment

Ke Wang, Wenning Wei, Yan Deng +2

Automatic Pronunciation Assessment (APA) is critical for Computer-Assisted Language Learning (CALL), requiring evaluation across multiple granularities and aspects. Large Multimoda…

cs.SD2025

Exploring the Potential of Large Multimodal Models as Effective Alternatives for Pronunciation Assessment

Ke Wang, Lei He, Kun Liu +3

Large Multimodal Models (LMMs) have demonstrated exceptional performance across a wide range of domains. This paper explores their potential in pronunciation assessment tasks, with…

eess.AS2025

CosyAudio: Improving Audio Generation with Confidence Scores and Synthetic Captions

Xinfa Zhu, Wenjie Tian, Xinsheng Wang +4

Text-to-Audio (TTA) generation is an emerging area within AI-generated content (AIGC), where audio is created from natural language descriptions. Despite growing interest, developi…

eess.AS2025

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training

Xinfa Zhu, Lei He, Yujia Xiao +4

Style voice conversion aims to transform the speaking style of source speech into a desired style while keeping the original speaker's identity. However, previous style voice conve…

eess.AS2023★ 1 cited

MuLanTTS: The Microsoft Speech Synthesis System for Blizzard Challenge 2023

Zhihang Xu, Shaofei Zhang, Xi Wang +4

In this paper, we present MuLanTTS, the Microsoft end-to-end neural text-to-speech (TTS) system designed for the Blizzard Challenge 2023. About 50 hours of audiobook corpus for Fre…

cs.SD2023★ 1 cited

Large-Scale Automatic Audiobook Creation

Brendan Walsh, Mark Hamilton, Greg Newby +8

An audiobook can dramatically improve a work of literature's accessibility and improve reader engagement. However, audiobooks can take hundreds of hours of human effort to create,…