Search Results for author:
Found 17 papers, 5 papers with code
AudioSR: Versatile Audio Super-resolution at Scale
Audio super-resolution is a fundamental task that predicts high-frequency components for low-resolution audio, enhancing audio quality in digital applications.
MSM-VC: High-fidelity Source Style Transfer for Non-Parallel Voice Conversion by Multi-scale Style Modeling
In addition to conveying the linguistic content from source speech to converted speech, maintaining the speaking style of source speech also plays an important role in the voice conversion (VC) task, which is essential in many scenarios with highly expressive source speech, such as dubbing and data augmentation.
AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining
Any audio can be translated into LOA based on AudioMAE, a self-supervised pre-trained representation learning model.
Ranked #3 on
Audio Generation
on AudioCaps
LM-VC: Zero-shot Voice Conversion via Speech Generation based on Language Models
An intuitive approach is to follow AudioLM - Tokenizing speech into semantic and acoustic tokens respectively by HuBERT and SoundStream, and converting source semantic tokens to target acoustic tokens conditioned on acoustic tokens of the target speaker.
PolyVoice: Language Models for Speech to Speech Translation
For the speech synthesis part, we adopt the existing VALL-E X approach and build a unit-based audio language model.
A unified front-end framework for English text-to-speech synthesis
The front-end is a critical component of English text-to-speech (TTS) systems, responsible for extracting linguistic features that are essential for a text-to-speech model to synthesize speech, such as prosodies and phonemes.
Multi-level Temporal-channel Speaker Retrieval for Zero-shot Voice Conversion
Specifically, to flexibly adapt to the dynamic-variant speaker characteristic in the temporal and channel axis of the speech, we propose a novel fine-grained speaker modeling method, called temporal-channel retrieval (TCR), to find out when and where speaker information appears in speech.
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.
Delivering Speaking Style in Low-resource Voice Conversion with Multi-factor Constraints
Conveying the linguistic content and maintaining the source speech's speaking style, such as intonation and emotion, is essential in voice conversion (VC).
Streaming Voice Conversion Via Intermediate Bottleneck Features And Non-streaming Teacher Guidance
In this paper, we propose to use intermediate bottleneck features (IBFs) to replace PPGs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
VoiceFixer: A Unified Framework for High-Fidelity Speech Restoration
Speech restoration aims to remove distortions in speech signals.
Neural Vocoder is All You Need for Speech Super-resolution
In this paper, we propose a neural vocoder based speech super-resolution method (NVSR) that can handle a variety of input resolution and upsampling ratios.
Ranked #2 on
Audio Super-Resolution
on VCTK Multi-Speaker
Neural Dubber: Dubbing for Videos According to Scripts
Neural Dubber is a multi-modal text-to-speech (TTS) model that utilizes the lip movement in the video to control the prosody of the generated speech.
Cloning one's voice using very limited data in the wild
(2) How to clone a person's voice while controlling the style and prosody.
TFGAN: Time and Frequency Domain Based Generative Adversarial Network for High-fidelity Speech Synthesis
On one hand, we propose to discriminate ground-truth waveform from synthetic one in frequency domain for offering more consistency guarantees instead of only in time domain.
AdaDurIAN: Few-shot Adaptation for Neural Text-to-Speech with DurIAN
This paper investigates how to leverage a DurIAN-based average model to enable a new speaker to have both accurate pronunciation and fluent cross-lingual speaking with very limited monolingual data.
Generative Adversarial Network based Speaker Adaptation for High Fidelity WaveNet Vocoder
Firstly, due to the noisy input signal of the model, there is still a gap between the quality of generated and natural waveforms.
Generative Adversarial Network
Vocal Bursts Intensity Prediction
