9 citations · 16 across the 12 of their papers we have counts for
12 papers
LISE : Listenable Interpretable Speaker Embeddings
Xiaoliang Wu, Chongxin Gan, Ke Liu +2
Deep neural network-based automatic speaker verification (ASV) systems achieve impressive performance but their embedding representations remain opaque, lacking a structured and pe…
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…
New Challenges for Content Privacy in Speech and Audio
Jennifer Williams, Karla Pizzi, Shuvayanti Das +1
Privacy in speech and audio has many facets. A particularly under-developed area of privacy in this domain involves consideration for information related to content and context. Sp…
Localized Shortcut Removal
Nicolas M. Müller, Jochen Jacobs, Jennifer Williams +1
Machine learning is a data-driven field, and the quality of the underlying datasets plays a crucial role in learning success. However, high performance on held-out test data does n…
Attacker Attribution of Audio Deepfakes
Nicolas M. Müller, Franziska Dieckmann, Jennifer Williams
Deepfakes are synthetically generated media often devised with malicious intent. They have become increasingly more convincing with large training datasets advanced neural networks…
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…
Comparison of Speech Representations for Automatic Quality Estimation in Multi-Speaker Text-to-Speech Synthesis
Jennifer Williams, Joanna Rownicka, Pilar Oplustil +1
We aim to characterize how different speakers contribute to the perceived output quality of multi-speaker Text-to-Speech (TTS) synthesis. We automatically rate the quality of TTS u…
Speech Replay Detection with x-Vector Attack Embeddings and Spectral Features
Jennifer Williams, Joanna Rownicka
We present our system submission to the ASVspoof 2019 Challenge Physical Access (PA) task. The objective for this challenge was to develop a countermeasure that identifies speech a…