67 citations · 211 across the 44 of their papers we have counts for
17 papers · 1 filter
Expressive paragraph text-to-speech synthesis with multi-step variational autoencoder
Xuyuan Li, Zengqiang Shang, Peiyang Shi +3
Neural networks have been able to generate high-quality single-sentence speech. However, it remains a challenge concerning audio-book speech synthesis due to the intra-paragraph co…
Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain Adaptation Speaker Verification
Zhuo Li, Jingze Lu, Zhenduo Zhao +2
Utilizing the large-scale unlabeled data from the target domain via pseudo-label clustering algorithms is an important approach for addressing domain adaptation problems in speaker…
The HCCL system for VoxCeleb Speaker Recognition Challenge 2022
Zhenduo Zhao, Zhuo Li, Wenchao Wang +1
This report describes our submission to track1 and track3 for VoxCeleb Speaker Recognition Challenge 2022(VoxSRC2022). Our best system achieves minDCF 0.1397 and EER 2.414 in track…
ForkNet: Simultaneous Time and Time-Frequency Domain Modeling for Speech Enhancement
Feng Dang, Qi Hu, Pengyuan Zhang +1
Previous research in speech enhancement has mostly focused on modeling time or time-frequency domain information alone, with little consideration given to the potential benefits of…
PCF: ECAPA-TDNN with Progressive Channel Fusion for Speaker Verification
Zhenduo Zhao, Zhuo Li, Wenchao Wang +1
ECAPA-TDNN is currently the most popular TDNN-series model for speaker verification, which refreshed the state-of-the-art(SOTA) performance of TDNN models. However, one-dimensional…
THLNet: two-stage heterogeneous lightweight network for monaural speech enhancement
Feng Dang, Qi Hu, Pengyuan Zhang
In this paper, we propose a two-stage heterogeneous lightweight network for monaural speech enhancement. Specifically, we design a novel two-stage framework consisting of a coarse-…
Streaming non-autoregressive model for any-to-many voice conversion
Ziyi Chen, Haoran Miao, Pengyuan Zhang
Voice conversion models have developed for decades, and current mainstream research focuses on non-streaming voice conversion. However, streaming voice conversion is more suitable…
Back-ends Selection for Deep Speaker Embeddings
Zhuo Li, Runqiu Xiao, Zihan Zhang +3
Probabilistic Linear Discriminant Analysis (PLDA) was the dominant and necessary back-end for early speaker recognition approaches, like i-vector and x-vector. However, with the de…
CTA-RNN: Channel and Temporal-wise Attention RNN Leveraging Pre-trained ASR Embeddings for Speech Emotion Recognition
Chengxin Chen, Pengyuan Zhang
Previous research has looked into ways to improve speech emotion recognition (SER) by utilizing both acoustic and linguistic cues of speech. However, the potential association betw…
The HCCL-DKU system for fake audio generation task of the 2022 ICASSP ADD Challenge
Ziyi Chen, Hua Hua, Yuxiang Zhang +2
The voice conversion task is to modify the speaker identity of continuous speech while preserving the linguistic content. Generally, the naturalness and similarity are two main met…
Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition
Changfeng Gao, Gaofeng Cheng, Pengyuan Zhang
Automatic speech recognition (ASR) has shown rapid advances in recent years but still degrades significantly in far-field and noisy environments. The recent development of self-sup…
Adaptive Margin Circle Loss for Speaker Verification
Runqiu Xiao
Deep-Neural-Network (DNN) based speaker verification sys-tems use the angular softmax loss with margin penalties toenhance the intra-class compactness of speaker embeddings,which a…
DPT-FSNet: Dual-path Transformer Based Full-band and Sub-band Fusion Network for Speech Enhancement
Feng Dang, Hangting Chen, Pengyuan Zhang
Sub-band models have achieved promising results due to their ability to model local patterns in the spectrogram. Some studies further improve the performance by fusing sub-band and…
Power pooling: An adaptive pooling function for weakly labelled sound event detection
Yuzhuo Liu, Hangting Chen, YunWang +1
Access to large corpora with strongly labelled sound events is expensive and difficult in engineering applications. Much research turns to address the problem of how to detect both…
Power Pooling Operators and Confidence Learning for Semi-Supervised Sound Event Detection
Yuzhuo Liu, Hangting Chen, Pengyuan Zhang
In recent years, the involvement of synthetic strongly labeled data,weakly labeled data and unlabeled data has drawn much research attentionin semi-supervised sound event detection…
Utterance-level Permutation Invariant Training with Latency-controlled BLSTM for Single-channel Multi-talker Speech Separation
Lu Huang, Gaofeng Cheng, Pengyuan Zhang +3
Utterance-level permutation invariant training (uPIT) has achieved promising progress on single-channel multi-talker speech separation task. Long short-term memory (LSTM) and bidir…
Noise Robust IOA/CAS Speech Separation and Recognition System For The Third 'CHIME' Challenge
Xiaofei Wang, Chao Wu, Pengyuan Zhang +5
This paper presents the contribution to the third 'CHiME' speech separation and recognition challenge including both front-end signal processing and back-end speech recognition. In…