9 citations · 18 across the 7 of their papers we have counts for
7 papers
Expressive-VC: Highly Expressive Voice Conversion with Attention Fusion of Bottleneck and Perturbation Features
Ziqian Ning, Qicong Xie, Pengcheng Zhu +5
Voice conversion for highly expressive speech is challenging. Current approaches struggle with the balancing between speaker similarity, intelligibility and expressiveness. To addr…
AccentSpeech: Learning Accent from Crowd-sourced Data for Target Speaker TTS with Accents
Yongmao Zhang, Zhichao Wang, Peiji Yang +3
Learning accent from crowd-sourced data is a feasible way to achieve a target speaker TTS system that can synthesize accent speech. To this end, there are two challenging problems…
Streaming Voice Conversion Via Intermediate Bottleneck Features And Non-streaming Teacher Guidance
Yuanzhe Chen, Ming Tu, Tang Li +7
Streaming voice conversion (VC) is the task of converting the voice of one person to another in real-time. Previous streaming VC methods use phonetic posteriorgrams (PPGs) extracte…
IQDUBBING: Prosody modeling based on discrete self-supervised speech representation for expressive voice conversion
Wendong Gan, Bolong Wen, Ying Yan +6
Prosody modeling is important, but still challenging in expressive voice conversion. As prosody is difficult to model, and other factors, e.g., speaker, environment and content, wh…
Enriching Source Style Transfer in Recognition-Synthesis based Non-Parallel Voice Conversion
Zhichao Wang, Xinyong Zhou, Fengyu Yang +6
Current voice conversion (VC) methods can successfully convert timbre of the audio. As modeling source audio's prosody effectively is a challenging task, there are still limitation…
WNARS: WFST based Non-autoregressive Streaming End-to-End Speech Recognition
Zhichao Wang, Wenwen Yang, Pan Zhou +1
Recently, attention-based encoder-decoder (AED) end-to-end (E2E) models have drawn more and more attention in the field of automatic speech recognition (ASR). AED models, however,…