195 citations · 227 across the 10 of their papers we have counts for
4 papers · 1 filter
REDAT: Accent-Invariant Representation for End-to-End ASR by Domain Adversarial Training with Relabeling
Hu Hu, Xuesong Yang, Zeynab Raeesy +6
Accents mismatching is a critical problem for end-to-end ASR. This paper aims to address this problem by building an accent-robust RNN-T system with domain adversarial training (DA…
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
Kaizhi Qian, Yang Zhang, Shiyu Chang +2
Non-parallel many-to-many voice conversion, as well as zero-shot voice conversion, remain under-explored areas. Deep style transfer algorithms, such as generative adversarial netwo…
When CTC Training Meets Acoustic Landmarks
Di He, Xuesong Yang, Boon Pang Lim +3
Connectionist temporal classification (CTC) provides an end-to-end acoustic model (AM) training strategy. CTC learns accurate AMs without time-aligned phonetic transcription, but s…
Acoustic Landmarks Contain More Information About the Phone String than Other Frames for Automatic Speech Recognition with Deep Neural Network Acoustic Model
Di He, Boon Pang Lim, Xuesong Yang +2
Most mainstream Automatic Speech Recognition (ASR) systems consider all feature frames equally important. However, acoustic landmark theory is based on a contradictory idea, that s…