3 citations · 5 across the 7 of their papers we have counts for
6 papers · 1 filter
Disentangling Speaker Traits for Deepfake Source Verification via Chebyshev Polynomial and Riemannian Metric Learning
Xi Xuan, Wenxin Zhang, Zhiyu Li +3
Speech deepfake source verification systems aims to determine whether two synthetic speech utterances originate from the same source generator, often assuming that the resulting so…
Analysis of Voice Conversion and Code-Switching Synthesis Using VQ-VAE
Shuvayanti Das, Jennifer Williams, Catherine Lai
This paper presents an analysis of speech synthesis quality achieved by simultaneously performing voice conversion and language code-switching using multilingual VQ-VAE speech synt…
Revisiting Speech Content Privacy
Jennifer Williams, Junichi Yamagishi, Paul-Gauthier Noe +2
In this paper, we discuss an important aspect of speech privacy: protecting spoken content. New capabilities from the field of machine learning provide a unique and timely opportun…
Exploring Disentanglement with Multilingual and Monolingual VQ-VAE
Jennifer Williams, Jason Fong, Erica Cooper +1
This work examines the content and usefulness of disentangled phone and speaker representations from two separately trained VQ-VAE systems: one trained on multilingual data and ano…
Learning Disentangled Phone and Speaker Representations in a Semi-Supervised VQ-VAE Paradigm
Jennifer Williams, Yi Zhao, Erica Cooper +1
We present a new approach to disentangle speaker voice and phone content by introducing new components to the VQ-VAE architecture for speech synthesis. The original VQ-VAE does not…
Improved Prosody from Learned F0 Codebook Representations for VQ-VAE Speech Waveform Reconstruction
Yi Zhao, Haoyu Li, Cheng-I Lai +3
Vector Quantized Variational AutoEncoders (VQ-VAE) are a powerful representation learning framework that can discover discrete groups of features from a speech signal without super…