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Found 12 papers, 6 papers with code
VoiceShop: A Unified Speech-to-Speech Framework for Identity-Preserving Zero-Shot Voice Editing
We present VoiceShop, a novel speech-to-speech framework that can modify multiple attributes of speech, such as age, gender, accent, and speech style, in a single forward pass while preserving the input speaker's timbre.
Zero-Shot Accent Conversion using Pseudo Siamese Disentanglement Network
The goal of accent conversion (AC) is to convert the accent of speech into the target accent while preserving the content and speaker identity.
Parallel Neural Text-to-Speech
In this work, we first propose ParaNet, a non-autoregressive seq2seq model that converts text to spectrogram.
WaveFlow: A Compact Flow-based Model for Raw Audio
WaveFlow provides a unified view of likelihood-based models for 1-D data, including WaveNet and WaveGlow as special cases.
Ranked #13 on
Speech Synthesis
on LibriTTS
Incremental Text-to-Speech Synthesis with Prefix-to-Prefix Framework
Text-to-speech synthesis (TTS) has witnessed rapid progress in recent years, where neural methods became capable of producing audios with high naturalness.
Multi-Speaker End-to-End Speech Synthesis
In this work, we extend ClariNet (Ping et al., 2019), a fully end-to-end speech synthesis model (i. e., text-to-wave), to generate high-fidelity speech from multiple speakers.
Non-Autoregressive Neural Text-to-Speech
In this work, we propose ParaNet, a non-autoregressive seq2seq model that converts text to spectrogram.
ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech
In this work, we propose a new solution for parallel wave generation by WaveNet.
Neural Voice Cloning with a Few Samples
Speaker adaptation is based on fine-tuning a multi-speaker generative model with a few cloning samples.
HybridNet: A Hybrid Neural Architecture to Speed-up Autoregressive Models
This paper introduces HybridNet, a hybrid neural network to speed-up autoregressive models for raw audio waveform generation.
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
We present Deep Voice 3, a fully-convolutional attention-based neural text-to-speech (TTS) system.
Deep Voice 2: Multi-Speaker Neural Text-to-Speech
We introduce Deep Voice 2, which is based on a similar pipeline with Deep Voice 1, but constructed with higher performance building blocks and demonstrates a significant audio quality improvement over Deep Voice 1.
