24 citations · 39 across the 18 of their papers we have counts for
9 papers · 1 filter
Speech Codec Probing from Semantic and Phonetic Perspectives
Xuan Shi, Chang Zeng, Tiantian Feng +3
Speech tokenizers are essential for connecting speech to large language models (LLMs) in multimodal systems. Speech tokenizers are expected to preserve both semantic and acoustic i…
InstructSing: High-Fidelity Singing Voice Generation via Instructing Yourself
Chang Zeng, Chunhui Wang, Xiaoxiao Miao +3
It is challenging to accelerate the training process while ensuring both high-quality generated voices and acceptable inference speed. In this paper, we propose a novel neural voco…
Spoofing-Aware Speaker Verification Robust Against Domain and Channel Mismatches
Chang Zeng, Xiaoxiao Miao, Xin Wang +2
In real-world applications, it is challenging to build a speaker verification system that is simultaneously robust against common threats, including spoofing attacks, channel misma…
Improving Generalization Ability of Countermeasures for New Mismatch Scenario by Combining Multiple Advanced Regularization Terms
Chang Zeng, Xin Wang, Xiaoxiao Miao +2
The ability of countermeasure models to generalize from seen speech synthesis methods to unseen ones has been investigated in the ASVspoof challenge. However, a new mismatch scenar…
Xiaoicesing 2: A High-Fidelity Singing Voice Synthesizer Based on Generative Adversarial Network
Chunhui Wang, Chang Zeng, Xing He
XiaoiceSing is a singing voice synthesis (SVS) system that aims at generating 48kHz singing voices. However, the mel-spectrogram generated by it is over-smoothing in middle- and hi…
HiFi-WaveGAN: Generative Adversarial Network with Auxiliary Spectrogram-Phase Loss for High-Fidelity Singing Voice Generation
Chunhui Wang, Chang Zeng, Jun Chen +1
Entertainment-oriented singing voice synthesis (SVS) requires a vocoder to generate high-fidelity (e.g. 48kHz) audio. However, most text-to-speech (TTS) vocoders cannot reconstruct…
Joint Speaker Encoder and Neural Back-end Model for Fully End-to-End Automatic Speaker Verification with Multiple Enrollment Utterances
Chang Zeng, Xiaoxiao Miao, Xin Wang +2
Conventional automatic speaker verification systems can usually be decomposed into a front-end model such as time delay neural network (TDNN) for extracting speaker embeddings and…
Spoofing-Aware Attention based ASV Back-end with Multiple Enrollment Utterances and a Sampling Strategy for the SASV Challenge 2022
Chang Zeng, Lin Zhang, Meng Liu +1
Current state-of-the-art automatic speaker verification (ASV) systems are vulnerable to presentation attacks, and several countermeasures (CMs), which distinguish bona fide trials…
Attention Back-end for Automatic Speaker Verification with Multiple Enrollment Utterances
Chang Zeng, Xin Wang, Erica Cooper +2
Probabilistic linear discriminant analysis (PLDA) or cosine similarity have been widely used in traditional speaker verification systems as back-end techniques to measure pairwise…