9 citations · 9 across the 3 of their papers we have counts for
6 papers
Expressive Machine Dubbing Through Phrase-level Cross-lingual Prosody Transfer
Jakub Swiatkowski, Duo Wang, Mikolaj Babianski +5
Speech generation for machine dubbing adds complexity to conventional Text-To-Speech solutions as the generated output is required to match the expressiveness, emotion and speaking…
Cross-lingual Prosody Transfer for Expressive Machine Dubbing
Jakub Swiatkowski, Duo Wang, Mikolaj Babianski +3
Prosody transfer is well-studied in the context of expressive speech synthesis. Cross-lingual prosody transfer, however, is challenging and has been under-explored to date. In this…
CN-Celeb: multi-genre speaker recognition
Lantian Li, Ruiqi Liu, Jiawen Kang +6
Research on speaker recognition is extending to address the vulnerability in the wild conditions, among which genre mismatch is perhaps the most challenging, for instance, enrollme…
Bunched LPCNet : Vocoder for Low-cost Neural Text-To-Speech Systems
Ravichander Vipperla, Sangjun Park, Kihyun Choo +6
LPCNet is an efficient vocoder that combines linear prediction and deep neural network modules to keep the computational complexity low. In this work, we present two techniques to…
Iterative Compression of End-to-End ASR Model using AutoML
Abhinav Mehrotra, Łukasz Dudziak, Jinsu Yeo +9
Increasing demand for on-device Automatic Speech Recognition (ASR) systems has resulted in renewed interests in developing automatic model compression techniques. Past research hav…
ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning
Łukasz Dudziak, Mohamed S. Abdelfattah, Ravichander Vipperla +2
End-to-end automatic speech recognition (ASR) models are increasingly large and complex to achieve the best possible accuracy. In this paper, we build an AutoML system that uses re…