58 citations · 150 across the 81 of their papers we have counts for
36 papers · 1 filter
RSET: Remapping-based Sorting Method for Emotion Transfer Speech Synthesis
Haoxiang Shi, Jianzong Wang, Xulong Zhang +3
Although current Text-To-Speech (TTS) models are able to generate high-quality speech samples, there are still challenges in developing emotion intensity controllable TTS. Most exi…
MAIN-VC: Lightweight Speech Representation Disentanglement for One-shot Voice Conversion
Pengcheng Li, Jianzong Wang, Xulong Zhang +3
One-shot voice conversion aims to change the timbre of any source speech to match that of the unseen target speaker with only one speech sample. Existing methods face difficulties…
Learning Expressive Disentangled Speech Representations with Soft Speech Units and Adversarial Style Augmentation
Yimin Deng, Jianzong Wang, Xulong Zhang +2
Voice conversion is the task to transform voice characteristics of source speech while preserving content information. Nowadays, self-supervised representation learning models are…
EfficientASR: Speech Recognition Network Compression via Attention Redundancy and Chunk-Level FFN Optimization
Jianzong Wang, Ziqi Liang, Xulong Zhang +2
In recent years, Transformer networks have shown remarkable performance in speech recognition tasks. However, their deployment poses challenges due to high computational and storag…
EAD-VC: Enhancing Speech Auto-Disentanglement for Voice Conversion with IFUB Estimator and Joint Text-Guided Consistent Learning
Ziqi Liang, Jianzong Wang, Xulong Zhang +3
Using unsupervised learning to disentangle speech into content, rhythm, pitch, and timbre for voice conversion has become a hot research topic. Existing works generally take into a…
CONTUNER: Singing Voice Beautifying with Pitch and Expressiveness Condition
Jianzong Wang, Pengcheng Li, Xulong Zhang +2
Singing voice beautifying is a novel task that has application value in people's daily life, aiming to correct the pitch of the singing voice and improve the expressiveness without…
Learning Disentangled Speech Representations with Contrastive Learning and Time-Invariant Retrieval
Yimin Deng, Huaizhen Tang, Xulong Zhang +3
Voice conversion refers to transferring speaker identity with well-preserved content. Better disentanglement of speech representations leads to better voice conversion. Recent stud…
CLN-VC: Text-Free Voice Conversion Based on Fine-Grained Style Control and Contrastive Learning with Negative Samples Augmentation
Yimin Deng, Xulong Zhang, Jianzong Wang +2
Better disentanglement of speech representation is essential to improve the quality of voice conversion. Recently contrastive learning is applied to voice conversion successfully b…
DQR-TTS: Semi-supervised Text-to-speech Synthesis with Dynamic Quantized Representation
Jianzong Wang, Pengcheng Li, Xulong Zhang +2
Most existing neural-based text-to-speech methods rely on extensive datasets and face challenges under low-resource condition. In this paper, we introduce a novel semi-supervised t…
Contrastive Latent Space Reconstruction Learning for Audio-Text Retrieval
Kaiyi Luo, Xulong Zhang, Jianzong Wang +3
Cross-modal retrieval (CMR) has been extensively applied in various domains, such as multimedia search engines and recommendation systems. Most existing CMR methods focus on image-…
FastGraphTTS: An Ultrafast Syntax-Aware Speech Synthesis Framework
Jianzong Wang, Xulong Zhang, Aolan Sun +2
This paper integrates graph-to-sequence into an end-to-end text-to-speech framework for syntax-aware modelling with syntactic information of input text. Specifically, the input tex…
Voice Conversion with Denoising Diffusion Probabilistic GAN Models
Xulong Zhang, Jianzong Wang, Ning Cheng +1
Voice conversion is a method that allows for the transformation of speaking style while maintaining the integrity of linguistic information. There are many researchers using deep g…
Symbolic & Acoustic: Multi-domain Music Emotion Modeling for Instrumental Music
Kexin Zhu, Xulong Zhang, Jianzong Wang +2
Music Emotion Recognition involves the automatic identification of emotional elements within music tracks, and it has garnered significant attention due to its broad applicability…
PMVC: Data Augmentation-Based Prosody Modeling for Expressive Voice Conversion
Yimin Deng, Huaizhen Tang, Xulong Zhang +3
Voice conversion as the style transfer task applied to speech, refers to converting one person's speech into a new speech that sounds like another person's. Up to now, there has be…
EmoMix: Emotion Mixing via Diffusion Models for Emotional Speech Synthesis
Haobin Tang, Xulong Zhang, Jianzong Wang +2
There has been significant progress in emotional Text-To-Speech (TTS) synthesis technology in recent years. However, existing methods primarily focus on the synthesis of a limited…
SAR: Self-Supervised Anti-Distortion Representation for End-To-End Speech Model
Jianzong Wang, Xulong Zhang, Haobin Tang +3
In recent Text-to-Speech (TTS) systems, a neural vocoder often generates speech samples by solely conditioning on acoustic features predicted from an acoustic model. However, there…
Dynamic Alignment Mask CTC: Improved Mask-CTC with Aligned Cross Entropy
Xulong Zhang, Haobin Tang, Jianzong Wang +3
Because of predicting all the target tokens in parallel, the non-autoregressive models greatly improve the decoding efficiency of speech recognition compared with traditional autor…
QI-TTS: Questioning Intonation Control for Emotional Speech Synthesis
Haobin Tang, Xulong Zhang, Jianzong Wang +2
Recent expressive text to speech (TTS) models focus on synthesizing emotional speech, but some fine-grained styles such as intonation are neglected. In this paper, we propose QI-TT…
Improving Music Genre Classification from Multi-Modal Properties of Music and Genre Correlations Perspective
Ganghui Ru, Xulong Zhang, Jianzong Wang +2
Music genre classification has been widely studied in past few years for its various applications in music information retrieval. Previous works tend to perform unsatisfactorily, s…
Semi-Supervised Learning Based on Reference Model for Low-resource TTS
Xulong Zhang, Jianzong Wang, Ning Cheng +1
Most previous neural text-to-speech (TTS) methods are mainly based on supervised learning methods, which means they depend on a large training dataset and hard to achieve comparabl…
MetaSpeech: Speech Effects Switch Along with Environment for Metaverse
Xulong Zhang, Jianzong Wang, Ning Cheng +1
Metaverse expands the physical world to a new dimension, and the physical environment and Metaverse environment can be directly connected and entered. Voice is an indispensable com…
Improving Speech Representation Learning via Speech-level and Phoneme-level Masking Approach
Xulong Zhang, Jianzong Wang, Ning Cheng +2
Recovering the masked speech frames is widely applied in speech representation learning. However, most of these models use random masking in the pre-training. In this work, we prop…
Adapitch: Adaption Multi-Speaker Text-to-Speech Conditioned on Pitch Disentangling with Untranscribed Data
Xulong Zhang, Jianzong Wang, Ning Cheng +1
In this paper, we proposed Adapitch, a multi-speaker TTS method that makes adaptation of the supervised module with untranscribed data. We design two self supervised modules to tra…
TGAVC: Improving Autoencoder Voice Conversion with Text-Guided and Adversarial Training
Huaizhen Tang, Xulong Zhang, Jianzong Wang +4
Non-parallel many-to-many voice conversion remains an interesting but challenging speech processing task. Recently, AutoVC, a conditional autoencoder based method, achieved excelle…