Search Results for author:
Found 34 papers, 15 papers with code
Text-to-Song: Towards Controllable Music Generation Incorporating Vocals and Accompaniment
A song is a combination of singing voice and accompaniment.
3D-Speaker-Toolkit: An Open Source Toolkit for Multi-modal Speaker Verification and Diarization
This paper introduces 3D-Speaker-Toolkit, an open source toolkit for multi-modal speaker verification and diarization.
Prompt-Singer: Controllable Singing-Voice-Synthesis with Natural Language Prompt
Recent singing-voice-synthesis (SVS) methods have achieved remarkable audio quality and naturalness, yet they lack the capability to control the style attributes of the synthesized singing explicitly.
Language-Codec: Reducing the Gaps Between Discrete Codec Representation and Speech Language Models
Furthermore, we also validate the efficiency of the Language-Codec on downstream speech language models.
Real3D-Portrait: One-shot Realistic 3D Talking Portrait Synthesis
One-shot 3D talking portrait generation aims to reconstruct a 3D avatar from an unseen image, and then animate it with a reference video or audio to generate a talking portrait video.
TransFace: Unit-Based Audio-Visual Speech Synthesizer for Talking Head Translation
However, talking head translation, converting audio-visual speech (i. e., talking head video) from one language into another, still confronts several challenges compared to audio speech: (1) Existing methods invariably rely on cascading, synthesizing via both audio and text, resulting in delays and cascading errors.
StyleSinger: Style Transfer for Out-of-Domain Singing Voice Synthesis
Moreover, existing SVS methods encounter a decline in the quality of synthesized singing voices in OOD scenarios, as they rest upon the assumption that the target vocal attributes are discernible during the training phase.
Speech-to-Speech Translation with Discrete-Unit-Based Style Transfer
Direct speech-to-speech translation (S2ST) with discrete self-supervised representations has achieved remarkable accuracy, but is unable to preserve the speaker timbre of the source speech during translation.
Mega-TTS: Zero-Shot Text-to-Speech at Scale with Intrinsic Inductive Bias
3) We further use a VQGAN-based acoustic model to generate the spectrogram and a latent code language model to fit the distribution of prosody, since prosody changes quickly over time in a sentence, and language models can capture both local and long-range dependencies.
Detector Guidance for Multi-Object Text-to-Image Generation
Previous works identify the problem of information mixing in the CLIP text encoder and introduce the T5 text encoder or incorporate strong prior knowledge to assist with the alignment.
Make-A-Voice: Unified Voice Synthesis With Discrete Representation
Various applications of voice synthesis have been developed independently despite the fact that they generate "voice" as output in common.
Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generation
Finally, we use LLMs to augment and transform a large amount of audio-label data into audio-text datasets to alleviate the problem of scarcity of temporal data.
Ranked #7 on
Audio Generation
on AudioCaps
AV-TranSpeech: Audio-Visual Robust Speech-to-Speech Translation
Direct speech-to-speech translation (S2ST) aims to convert speech from one language into another, and has demonstrated significant progress to date.
ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer
To mitigate the data scarcity in learning visual acoustic information, we 1) introduce a self-supervised learning framework to enhance both the visual-text encoder and denoiser decoder; 2) leverage the diffusion transformer scalable in terms of parameters and capacity to learn visual scene information.
Wav2SQL: Direct Generalizable Speech-To-SQL Parsing
Speech-to-SQL (S2SQL) aims to convert spoken questions into SQL queries given relational databases, which has been traditionally implemented in a cascaded manner while facing the following challenges: 1) model training is faced with the major issue of data scarcity, where limited parallel data is available; and 2) the systems should be robust enough to handle diverse out-of-domain speech samples that differ from the source data.
RMSSinger: Realistic-Music-Score based Singing Voice Synthesis
To tackle these challenges, we propose RMSSinger, the first RMS-SVS method, which takes realistic music scores as input, eliminating most of the tedious manual annotation and avoiding the aforementioned inconvenience.
AlignSTS: Speech-to-Singing Conversion via Cross-Modal Alignment
The speech-to-singing (STS) voice conversion task aims to generate singing samples corresponding to speech recordings while facing a major challenge: the alignment between the target (singing) pitch contour and the source (speech) content is difficult to learn in a text-free situation.
GeneFace++: Generalized and Stable Real-Time Audio-Driven 3D Talking Face Generation
Recently, neural radiance field (NeRF) has become a popular rendering technique in this field since it could achieve high-fidelity and 3D-consistent talking face generation with a few-minute-long training video.
AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head
In this work, we propose a multi-modal AI system named AudioGPT, which complements LLMs (i. e., ChatGPT) with 1) foundation models to process complex audio information and solve numerous understanding and generation tasks; and 2) the input/output interface (ASR, TTS) to support spoken dialogue.
MixSpeech: Cross-Modality Self-Learning with Audio-Visual Stream Mixup for Visual Speech Translation and Recognition
However, despite researchers exploring cross-lingual translation techniques such as machine translation and audio speech translation to overcome language barriers, there is still a shortage of cross-lingual studies on visual speech.
Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models
Its application to audio still lags behind for two main reasons: the lack of large-scale datasets with high-quality text-audio pairs, and the complexity of modeling long continuous audio data.
Ranked #11 on
Audio Generation
on AudioCaps
M4Singer: a Multi-Style, Multi-Singer and Musical Score Provided Mandarin Singing Corpus
The lack of publicly available high-quality and accurately labeled datasets has long been a major bottleneck for singing voice synthesis (SVS).
Diffusion Denoising Process for Perceptron Bias in Out-of-distribution Detection
In this paper, we introduce a new perceptron bias assumption that suggests discriminator models are more sensitive to certain features of the input, leading to the overconfidence problem.
VarietySound: Timbre-Controllable Video to Sound Generation via Unsupervised Information Disentanglement
In this paper, we pose the task of generating sound with a specific timbre given a video input and a reference audio sample.
ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech
Through the preliminary study on diffusion model parameterization, we find that previous gradient-based TTS models require hundreds or thousands of iterations to guarantee high sample quality, which poses a challenge for accelerating sampling.
TranSpeech: Speech-to-Speech Translation With Bilateral Perturbation
Specifically, a sequence of discrete representations derived in a self-supervised manner are predicted from the model and passed to a vocoder for speech reconstruction, while still facing the following challenges: 1) Acoustic multimodality: the discrete units derived from speech with same content could be indeterministic due to the acoustic property (e. g., rhythm, pitch, and energy), which causes deterioration of translation accuracy; 2) high latency: current S2ST systems utilize autoregressive models which predict each unit conditioned on the sequence previously generated, failing to take full advantage of parallelism.
GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech
Style transfer for out-of-domain (OOD) speech synthesis aims to generate speech samples with unseen style (e. g., speaker identity, emotion, and prosody) derived from an acoustic reference, while facing the following challenges: 1) The highly dynamic style features in expressive voice are difficult to model and transfer; and 2) the TTS models should be robust enough to handle diverse OOD conditions that differ from the source data.
FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis
Also, FastDiff enables a sampling speed of 58x faster than real-time on a V100 GPU, making diffusion models practically applicable to speech synthesis deployment for the first time.
Ranked #7 on
Text-To-Speech Synthesis
on LJSpeech
(using extra training data)
Multi-Singer: Fast Multi-Singer Singing Voice Vocoder With A Large-Scale Corpus
High-fidelity multi-singer singing voice synthesis is challenging for neural vocoder due to the singing voice data shortage, limited singer generalization, and large computational cost.
SingGAN: Generative Adversarial Network For High-Fidelity Singing Voice Generation
In this work, we propose SingGAN, a generative adversarial network designed for high-fidelity singing voice synthesis.
SynCLR: A Synthesis Framework for Contrastive Learning of out-of-domain Speech Representations
Learning generalizable speech representations for unseen samples in different domains has been a challenge with ever increasing importance to date.
Bilateral Denoising Diffusion Models
In this paper, we propose novel bilateral denoising diffusion models (BDDMs), which take significantly fewer steps to generate high-quality samples.
EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model
Finally, by showing a comparable performance in the emotional speech synthesis task, we successfully demonstrate the ability of the proposed model.
