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Found 9 papers, 4 papers with code
Voice Conversion from Non-parallel Corpora Using Variational Auto-encoder
We propose a flexible framework for spectral conversion (SC) that facilitates training with unaligned corpora.
Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks
Building a voice conversion (VC) system from non-parallel speech corpora is challenging but highly valuable in real application scenarios.
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.
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.
Dictionary Update for NMF-based Voice Conversion Using an Encoder-Decoder Network
In this paper, we propose a dictionary update method for Nonnegative Matrix Factorization (NMF) with high dimensional data in a spectral conversion (SC) task.
Quality-Net: An End-to-End Non-intrusive Speech Quality Assessment Model based on BLSTM
The evaluation of utterance-level quality in Quality-Net is based on the frame-level assessment.
Refined WaveNet Vocoder for Variational Autoencoder Based Voice Conversion
Conventional WaveNet vocoders are trained with natural acoustic features but conditioned on the converted features in the conversion stage for VC, and such a mismatch often causes significant quality and similarity degradation.
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.
