5 papers
Self-Supervised Audio-Visual Speech Representations Learning By Multimodal Self-Distillation
Jing-Xuan Zhang, Genshun Wan, Zhen-Hua Ling +3
In this work, we present a novel method, named AV2vec, for learning audio-visual speech representations by multimodal self-distillation. AV2vec has a student and a teacher module,…
Non-Parallel Sequence-to-Sequence Voice Conversion with Disentangled Linguistic and Speaker Representations
Jing-Xuan Zhang, Zhen-Hua Ling, Li-Rong Dai
This paper presents a method of sequence-to-sequence (seq2seq) voice conversion using non-parallel training data. In this method, disentangled linguistic and speaker representation…
Improving Sequence-to-Sequence Acoustic Modeling by Adding Text-Supervision
Jing-Xuan Zhang, Zhen-Hua Ling, Yuan Jiang +3
This paper presents methods of making using of text supervision to improve the performance of sequence-to-sequence (seq2seq) voice conversion. Compared with conventional frame-to-f…
Sequence-to-Sequence Acoustic Modeling for Voice Conversion
Jing-Xuan Zhang, Zhen-Hua Ling, Li-Juan Liu +2
In this paper, a neural network named Sequence-to-sequence ConvErsion NeTwork (SCENT) is presented for acoustic modeling in voice conversion. At training stage, a SCENT model is es…
Forward Attention in Sequence-to-sequence Acoustic Modelling for Speech Synthesis
Jing-Xuan Zhang, Zhen-Hua Ling, Li-Rong Dai
This paper proposes a forward attention method for the sequenceto- sequence acoustic modeling of speech synthesis. This method is motivated by the nature of the monotonic alignment…