Search Results for author:
Found 16 papers, 5 papers with code
Multi-objective Progressive Clustering for Semi-supervised Domain Adaptation in Speaker Verification
Utilizing the pseudo-labeling algorithm with large-scale unlabeled data becomes crucial for semi-supervised domain adaptation in speaker verification tasks.
Haha-Pod: An Attempt for Laughter-based Non-Verbal Speaker Verification
It is widely acknowledged that discriminative representation for speaker verification can be extracted from verbal speech.
The DKU-MSXF Speaker Verification System for the VoxCeleb Speaker Recognition Challenge 2023
For Track 1, we utilize a network structure based on ResNet for training.
The DKU-MSXF Diarization System for the VoxCeleb Speaker Recognition Challenge 2023
This paper describes the DKU-MSXF submission to track 4 of the VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC-23).
VoxBlink: A Large Scale Speaker Verification Dataset on Camera
In this paper, we introduce a large-scale and high-quality audio-visual speaker verification dataset, named VoxBlink.
Laugh Betrays You? Learning Robust Speaker Representation From Speech Containing Non-Verbal Fragments
We collect a set of clips with laughter components by conducting a laughter detection script on VoxCeleb and part of the CN-Celeb dataset.
Target-Speaker Voice Activity Detection via Sequence-to-Sequence Prediction
Target-speaker voice activity detection is currently a promising approach for speaker diarization in complex acoustic environments.
The DKU-DukeECE Diarization System for the VoxCeleb Speaker Recognition Challenge 2022
This paper discribes the DKU-DukeECE submission to the 4th track of the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22).
The 2022 Far-field Speaker Verification Challenge: Exploring domain mismatch and semi-supervised learning under the far-field scenario
In addition, a supplementary set for the FFSVC2020 dataset is released this year.
The DKU-OPPO System for the 2022 Spoofing-Aware Speaker Verification Challenge
For CM systems, we propose two methods on top of the challenge baseline to further improve the performance, namely Embedding Random Sampling Augmentation (ERSA) and One-Class Confusion Loss(OCCL).
Cross-Age Speaker Verification: Learning Age-Invariant Speaker Embeddings
In this paper, we mine cross-age test sets based on the VoxCeleb dataset and propose our age-invariant speaker representation(AISR) learning method.
Cross-Channel Attention-Based Target Speaker Voice Activity Detection: Experimental Results for M2MeT Challenge
The multi-channel TS-VAD further reduces the DER by 28% and achieves a DER of 2. 26%.
SIG-VC: A Speaker Information Guided Zero-shot Voice Conversion System for Both Human Beings and Machines
Moreover, speaker information control is added to our system to maintain the voice cloning performance.
Building Bilingual and Code-Switched Voice Conversion with Limited Training Data Using Embedding Consistency Loss
We achieve cross-lingual VC between Mandarin speech with multiple speakers and English speech with multiple speakers by applying bilingual bottleneck features.
Binary Neural Network for Speaker Verification
Although deep neural networks are successful for many tasks in the speech domain, the high computational and memory costs of deep neural networks make it difficult to directly deploy highperformance Neural Network systems on low-resource embedded devices.
Polyphone Disambiguation for Mandarin Chinese Using Conditional Neural Network with Multi-level Embedding Features
This paper describes a conditional neural network architecture for Mandarin Chinese polyphone disambiguation.
