9 citations · 25 across the 10 of their papers we have counts for
12 papers
Bridging the Gap Between Semantic and User Preference Spaces for Multi-modal Music Representation Learning
Xiaofeng Pan, Jing Chen, Haitong Zhang +4
Recent works of music representation learning mainly focus on learning acoustic music representations with unlabeled audios or further attempt to acquire multi-modal music represen…
DGC-vector: A new speaker embedding for zero-shot voice conversion
Ruitong Xiao, Haitong Zhang, Yue Lin
Recently, more and more zero-shot voice conversion algorithms have been proposed. As a fundamental part of zero-shot voice conversion, speaker embeddings are the key to improving t…
Improve few-shot voice cloning using multi-modal learning
Haitong Zhang, Yue Lin
Recently, few-shot voice cloning has achieved a significant improvement. However, most models for few-shot voice cloning are single-modal, and multi-modal few-shot voice cloning ha…
Revisiting IPA-based Cross-lingual Text-to-speech
Haitong Zhang, Haoyue Zhan, Yang Zhang +2
International Phonetic Alphabet (IPA) has been widely used in cross-lingual text-to-speech (TTS) to achieve cross-lingual voice cloning (CL VC). However, IPA itself has been unders…
Improve Cross-lingual Voice Cloning Using Low-quality Code-switched Data
Haitong Zhang, Yue Lin
Recently, sequence-to-sequence (seq-to-seq) models have been successfully applied in text-to-speech (TTS) to synthesize speech for single-language text. To synthesize speech for mu…
Exploring Timbre Disentanglement in Non-Autoregressive Cross-Lingual Text-to-Speech
Haoyue Zhan, Xinyuan Yu, Haitong Zhang +2
In this paper, we study the disentanglement of speaker and language representations in non-autoregressive cross-lingual TTS models from various aspects. We propose a phoneme length…
The NeteaseGames System for Voice Conversion Challenge 2020 with Vector-quantization Variational Autoencoder and WaveNet
Haitong Zhang
This paper presents the description of our submitted system for Voice Conversion Challenge (VCC) 2020 with vector-quantization variational autoencoder (VQ-VAE) with WaveNet as the…
Unsupervised Learning For Sequence-to-sequence Text-to-speech For Low-resource Languages
Haitong Zhang, Yue Lin
Recently, sequence-to-sequence models with attention have been successfully applied in Text-to-speech (TTS). These models can generate near-human speech with a large accurately-tra…
Improving Interpretability of Word Embeddings by Generating Definition and Usage
Haitong Zhang, Yongping Du, Jiaxin Sun +1
Word embeddings are substantially successful in capturing semantic relations among words. However, these lexical semantics are difficult to be interpreted. Definition modeling prov…
RawNet: Fast End-to-End Neural Vocoder
Yunchao He, Yujun Wang
Neural network-based vocoders have recently demonstrated the powerful ability to synthesize high-quality speech. These models usually generate samples by conditioning on spectral f…
End-to-end Models with auditory attention in Multi-channel Keyword Spotting
Haitong Zhang, Junbo Zhang, Yujun Wang
In this paper, we propose an attention-based end-to-end model for multi-channel keyword spotting (KWS), which is trained to optimize the KWS result directly. As a result, our model…
Sequence-to-sequence Models for Small-Footprint Keyword Spotting
Haitong Zhang, Junbo Zhang, Yujun Wang
In this paper, we propose a sequence-to-sequence model for keyword spotting (KWS). Compared with other end-to-end architectures for KWS, our model simplifies the pipelines of produ…