15 citations · 38 across the 15 of their papers we have counts for
15 papers
The Database and Benchmark for the Source Speaker Tracing Challenge 2024
Ze Li, Yuke Lin, Tian Yao +6
Voice conversion (VC) systems can transform audio to mimic another speaker's voice, thereby attacking speaker verification (SV) systems. However, ongoing studies on source speaker…
Self-supervised Reflective Learning through Self-distillation and Online Clustering for Speaker Representation Learning
Danwei Cai, Zexin Cai, Ze Li +1
Speaker representation learning is crucial for voice recognition systems, with recent advances in self-supervised approaches reducing dependency on labeled data. Current two-stage…
The DKU-DUKEECE System for the Manipulation Region Location Task of ADD 2023
Zexin Cai, Weiqing Wang, Yikang Wang +1
This paper introduces our system designed for Track 2, which focuses on locating manipulated regions, in the second Audio Deepfake Detection Challenge (ADD 2023). Our approach invo…
Waveform Boundary Detection for Partially Spoofed Audio
Zexin Cai, Weiqing Wang, Ming Li
The present paper proposes a waveform boundary detection system for audio spoofing attacks containing partially manipulated segments. Partially spoofed/fake audio, where part of th…
Identifying Source Speakers for Voice Conversion based Spoofing Attacks on Speaker Verification Systems
Danwei Cai, Zexin Cai, Ming Li
An automatic speaker verification system aims to verify the speaker identity of a speech signal. However, a voice conversion system could manipulate a person's speech signal to mak…
Invertible Voice Conversion
Zexin Cai, Ming Li
In this paper, we propose an invertible deep learning framework called INVVC for voice conversion. It is designed against the possible threats that inherently come along with voice…
SIG-VC: A Speaker Information Guided Zero-shot Voice Conversion System for Both Human Beings and Machines
Haozhe Zhang, Zexin Cai, Xiaoyi Qin +1
Nowadays, as more and more systems achieve good performance in traditional voice conversion (VC) tasks, people's attention gradually turns to VC tasks under extreme conditions. In…
The DKU System Description for The Interspeech 2021 Auto-KWS Challenge
Yechen Wang, Yan Jia, Murong Ma +2
This paper introduces the system submitted by the DKU-SMIIP team for the Auto-KWS 2021 Challenge. Our implementation consists of a two-stage keyword spotting system based on query-…
Training Wake Word Detection with Synthesized Speech Data on Confusion Words
Yan Jia, Zexin Cai, Murong Ma +4
Confusing-words are commonly encountered in real-life keyword spotting applications, which causes severe degradation of performance due to complex spoken terms and various kinds of…
Cross-lingual Multispeaker Text-to-Speech under Limited-Data Scenario
Zexin Cai, Yaogen Yang, Ming Li
Modeling voices for multiple speakers and multiple languages in one text-to-speech system has been a challenge for a long time. This paper presents an extension on Tacotron2 to ach…
From Speaker Verification to Multispeaker Speech Synthesis, Deep Transfer with Feedback Constraint
Zexin Cai, Chuxiong Zhang, Ming Li
High-fidelity speech can be synthesized by end-to-end text-to-speech models in recent years. However, accessing and controlling speech attributes such as speaker identity, prosody,…
Polyphone Disambiguation for Mandarin Chinese Using Conditional Neural Network with Multi-level Embedding Features
Zexin Cai, Yaogen Yang, Chuxiong Zhang +2
This paper describes a conditional neural network architecture for Mandarin Chinese polyphone disambiguation. The system is composed of a bidirectional recurrent neural network com…
End-to-end Language Identification using NetFV and NetVLAD
Jinkun Chen, Weicheng Cai, Danwei Cai +3
In this paper, we apply the NetFV and NetVLAD layers for the end-to-end language identification task. NetFV and NetVLAD layers are the differentiable implementations of the standar…
A Novel Learnable Dictionary Encoding Layer for End-to-End Language Identification
Weicheng Cai, Zexin Cai, Xiang Zhang +2
A novel learnable dictionary encoding layer is proposed in this paper for end-to-end language identification. It is inline with the conventional GMM i-vector approach both theoreti…
Insights into End-to-End Learning Scheme for Language Identification
Weicheng Cai, Zexin Cai, Wenbo Liu +2
A novel interpretable end-to-end learning scheme for language identification is proposed. It is in line with the classical GMM i-vector methods both theoretically and practically.…