1.2k citations · 2.5k across the 57 of their papers we have counts for
10 papers · 1 filter
Building Sequential Inference Models for End-to-End Response Selection
Jia-Chen Gu, Zhen-Hua Ling, Yu-Ping Ruan +1
This paper presents an end-to-end response selection model for Track 1 of the 7th Dialogue System Technology Challenges (DSTC7). This task focuses on selecting the correct next utt…
Learning latent representations for style control and transfer in end-to-end speech synthesis
Ya-Jie Zhang, Shifeng Pan, Lei He +1
In this paper, we introduce the Variational Autoencoder (VAE) to an end-to-end speech synthesis model, to learn the latent representation of speaking styles in an unsupervised mann…
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…
Enhancing Sentence Embedding with Generalized Pooling
Qian Chen, Zhen-Hua Ling, Xiaodan Zhu
Pooling is an essential component of a wide variety of sentence representation and embedding models. This paper explores generalized pooling methods to enhance sentence embedding.…
Hybrid semi-Markov CRF for Neural Sequence Labeling
Zhi-Xiu Ye, Zhen-Hua Ling
This paper proposes hybrid semi-Markov conditional random fields (SCRFs) for neural sequence labeling in natural language processing. Based on conventional conditional random field…
The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods
Jaime Lorenzo-Trueba, Junichi Yamagishi, Tomoki Toda +4
We present the Voice Conversion Challenge 2018, designed as a follow up to the 2016 edition with the aim of providing a common framework for evaluating and comparing different stat…
A Spoofing Benchmark for the 2018 Voice Conversion Challenge: Leveraging from Spoofing Countermeasures for Speech Artifact Assessment
Tomi Kinnunen, Jaime Lorenzo-Trueba, Junichi Yamagishi +4
Voice conversion (VC) aims at conversion of speaker characteristic without altering content. Due to training data limitations and modeling imperfections, it is difficult to achieve…
Waveform Modeling and Generation Using Hierarchical Recurrent Neural Networks for Speech Bandwidth Extension
Zhen-Hua Ling, Yang Ai, Yu Gu +1
This paper presents a waveform modeling and generation method using hierarchical recurrent neural networks (HRNN) for speech bandwidth extension (BWE). Different from conventional…