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Found 21 papers, 8 papers with code
Collection and Analysis of Dialogues Provided by Two Speakers Acting as One
We are studying a cooperation style where multiple speakers can provide both advanced dialogue services and operator education.
FastVoiceGrad: One-step Diffusion-Based Voice Conversion with Adversarial Conditional Diffusion Distillation
Diffusion-based voice conversion (VC) techniques such as VoiceGrad have attracted interest because of their high VC performance in terms of speech quality and speaker similarity.
Training Generative Adversarial Network-Based Vocoder with Limited Data Using Augmentation-Conditional Discriminator
A generative adversarial network (GAN)-based vocoder trained with an adversarial discriminator is commonly used for speech synthesis because of its fast, lightweight, and high-quality characteristics.
iSTFTNet2: Faster and More Lightweight iSTFT-Based Neural Vocoder Using 1D-2D CNN
Owing to the difficulty of a 1D CNN to model high-dimensional spectrograms, the frequency dimension is reduced via temporal upsampling.
Wave-U-Net Discriminator: Fast and Lightweight Discriminator for Generative Adversarial Network-Based Speech Synthesis
This architecture provides a generator with sufficiently rich information for the synthesized speech to be closely matched to the real speech.
iSTFTNet: Fast and Lightweight Mel-Spectrogram Vocoder Incorporating Inverse Short-Time Fourier Transform
In recent text-to-speech synthesis and voice conversion systems, a mel-spectrogram is commonly applied as an intermediate representation, and the necessity for a mel-spectrogram vocoder is increasing.
MaskCycleGAN-VC: Learning Non-parallel Voice Conversion with Filling in Frames
With FIF, we apply a temporal mask to the input mel-spectrogram and encourage the converter to fill in missing frames based on surrounding frames.
CycleGAN-VC3: Examining and Improving CycleGAN-VCs for Mel-spectrogram Conversion
To address this, we examined the applicability of CycleGAN-VC/VC2 to mel-spectrogram conversion.
Nonparallel Voice Conversion with Augmented Classifier Star Generative Adversarial Networks
We previously proposed a method that allows for nonparallel voice conversion (VC) by using a variant of generative adversarial networks (GANs) called StarGAN.
Many-to-Many Voice Transformer Network
The main idea we propose is an extension of the original VTN that can simultaneously learn mappings among multiple speakers.
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.
StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion
To bridge this gap, we rethink conditional methods of StarGAN-VC, which are key components for achieving non-parallel multi-domain VC in a single model, and propose an improved variant called StarGAN-VC2.
Crossmodal Voice Conversion
We use the latent code of an input face image encoded by the face encoder as the auxiliary input into the speech converter and train the speech converter so that the original latent code can be recovered from the generated speech by the voice encoder.
CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion
Non-parallel voice conversion (VC) is a technique for learning the mapping from source to target speech without relying on parallel data.
WaveCycleGAN2: Time-domain Neural Post-filter for Speech Waveform Generation
WaveCycleGAN has recently been proposed to bridge the gap between natural and synthesized speech waveforms in statistical parametric speech synthesis and provides fast inference with a moving average model rather than an autoregressive model and high-quality speech synthesis with the adversarial training.
AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms
This paper describes a method based on a sequence-to-sequence learning (Seq2Seq) with attention and context preservation mechanism for voice conversion (VC) tasks.
ConvS2S-VC: Fully convolutional sequence-to-sequence voice conversion
Second, it achieves many-to-many conversion by simultaneously learning mappings among multiple speakers using only a single model instead of separately learning mappings between each speaker pair using a different model.
WaveCycleGAN: Synthetic-to-natural speech waveform conversion using cycle-consistent adversarial networks
The experimental results demonstrate that our proposed method can 1) alleviate the over-smoothing effect of the acoustic features despite the direct modification method used for the waveform and 2) greatly improve the naturalness of the generated speech sounds.
ACVAE-VC: Non-parallel many-to-many voice conversion with auxiliary classifier variational autoencoder
Such situations can be avoided by introducing an auxiliary classifier and training the encoder and decoder so that the attribute classes of the decoder outputs are correctly predicted by the classifier.
StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks
This paper proposes a method that allows non-parallel many-to-many voice conversion (VC) by using a variant of a generative adversarial network (GAN) called StarGAN.
Generative adversarial network-based approach to signal reconstruction from magnitude spectrograms
In this paper, we address the problem of reconstructing a time-domain signal (or a phase spectrogram) solely from a magnitude spectrogram.
