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Found 11 papers, 2 papers with code
Voice Conversion Based on Cross-Domain Features Using Variational Auto Encoders
An effective approach to non-parallel voice conversion (VC) is to utilize deep neural networks (DNNs), specifically variational auto encoders (VAEs), to model the latent structure of speech in an unsupervised manner.
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Spoofing attacks within a logical access (LA) scenario are generated with the latest speech synthesis and voice conversion technologies, including state-of-the-art neural acoustic and waveform model techniques.
Unsupervised Representation Disentanglement using Cross Domain Features and Adversarial Learning in Variational Autoencoder based Voice Conversion
In this paper, we extend the CDVAE-VC framework by incorporating the concept of adversarial learning, in order to further increase the degree of disentanglement, thereby improving the quality and similarity of converted speech.
The Academia Sinica Systems of Voice Conversion for VCC2020
This paper describes the Academia Sinica systems for the two tasks of Voice Conversion Challenge 2020, namely voice conversion within the same language (Task 1) and cross-lingual voice conversion (Task 2).
The AS-NU System for the M2VoC Challenge
The first track focuses on using a small number of 100 target utterances for voice cloning, while the second track focuses on using only 5 target utterances for voice cloning.
A Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion
First, a powerful parallel sequence-to-sequence model converts the input dysarthric speech into a normal speech of a reference speaker as an intermediate product, and a nonparallel, frame-wise VC model realized with a variational autoencoder then converts the speaker identity of the reference speech back to that of the patient while assumed to be capable of preserving the enhanced quality.
Relational Data Selection for Data Augmentation of Speaker-dependent Multi-band MelGAN Vocoder
Nowadays, neural vocoders can generate very high-fidelity speech when a bunch of training data is available.
Dual-Path Filter Network: Speaker-Aware Modeling for Speech Separation
DPFN is composed of two parts: the speaker module and the separation module.
SVSNet: An End-to-end Speaker Voice Similarity Assessment Model
Neural evaluation metrics derived for numerous speech generation tasks have recently attracted great attention.
Time Alignment using Lip Images for Frame-based Electrolaryngeal Voice Conversion
Voice conversion (VC) is an effective approach to electrolaryngeal (EL) speech enhancement, a task that aims to improve the quality of the artificial voice from an electrolarynx device.
Generation of Speaker Representations Using Heterogeneous Training Batch Assembly
In traditional speaker diarization systems, a well-trained speaker model is a key component to extract representations from consecutive and partially overlapping segments in a long speech session.
