Speech Synthesis Paper
List of speech synthesis papers (-> more papers <-). Welcome to recommend more awesome papers
Repositories for collecting awesome speech paper:
- awesome-speech-recognition-speech-synthesis-papers (from ponyzhang)
- awesome-python-scientific-audio (from Fabian-Robert Stรถter)
- TTS-papers (from Eren Gรถlge)
- awesome-speech-enhancement (from Vincent Liu)
- speech-recognition-papers (from Xingchen Song)
- awesome-tts-samples (from Seung-won Park)
- awesome-speech-translation (from dqqcasia)
- A Survey on Neural Speech Synthesis (from tts-tutorial)
What is the meaning of 'โ
'? I add 'โ
' to the papers which number of citations is over 50 (only in Acoustic Model, Vocoder and TTS towards Stylization). Beginner can read these paper first to get basic knowledge of Deep-Learning-based TTS model (#1).
Content
TTS Frontend
- Pre-trained Text Representations for Improving Front-End Text Processing in Mandarin Text-to-Speech Synthesis (Interspeech 2019)
- A unified sequence-to-sequence front-end model for Mandarin text-to-speech synthesis (ICASSP 2020)
- A hybrid text normalization system using multi-head self-attention for mandarin (ICASSP 2020)
- Unified Mandarin TTS Front-end Based on Distilled BERT Model (2021-01)
Acoustic Model
Autoregressive Model
- Tacotron V1โ : Tacotron: Towards End-to-End Speech Synthesis (Interspeech 2017)
- Tacotron V2โ : Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions (ICASSP 2018)
- Deep Voice V1โ : Deep Voice: Real-time Neural Text-to-Speech (ICML 2017)
- Deep Voice V2โ : Deep Voice 2: Multi-Speaker Neural Text-to-Speech (NeurIPS 2017)
- Deep Voice V3โ : Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning (ICLR 2018)
- Transformer-TTSโ : Neural Speech Synthesis with Transformer Network (AAAI 2019)
- DurIAN: DurIAN: Duration Informed Attention Network For Multimodal Synthesis (2019)
- Location-Relative Attention Mechanisms For Robust Long-Form Speech Synthesis (ICASSP 2020)
- Flowtron (flow based): Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis (2020)
- Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling (under review ICLR 2021)
- RobuTrans (towards robust): RobuTrans: A Robust Transformer-Based Text-to-Speech Model (AAAI 2020)
- DeviceTTS: DeviceTTS: A Small-Footprint, Fast, Stable Network for On-Device Text-to-Speech (2020-10)
- Wave-Tacotron: Wave-Tacotron: Spectrogram-free end-to-end text-to-speech synthesis (2020-11)
- Streaming Acoustic Modeling: Transformer-based Acoustic Modeling for Streaming Speech Synthesis (2021-06)
- Apple TTS system: On-device neural speech synthesis (ASRU 2021)
Non-Autoregressive Model
- ParaNet: Non-Autoregressive Neural Text-to-Speech (ICML 2020)
- FastSpeechโ : FastSpeech: Fast, Robust and Controllable Text to Speech (NeurIPS 2019)
- JDI-T: JDI-T: Jointly trained Duration Informed Transformer for Text-To-Speech without Explicit Alignment (2020)
- EATS: End-to-End Adversarial Text-to-Speech (2020)
- FastSpeech 2: FastSpeech 2: Fast and High-Quality End-to-End Text to Speech (2020)
- FastPitch: FastPitch: Parallel Text-to-speech with Pitch Prediction (2020)
- Glow-TTS (flow based, Monotonic Attention): Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search (NeurIPS 2020)
- Flow-TTS (flow based): Flow-TTS: A Non-Autoregressive Network for Text to Speech Based on Flow (ICASSP 2020)
- SpeedySpeech: SpeedySpeech: Efficient Neural Speech Synthesis (Interspeech 2020)
- Parallel Tacotron: Parallel Tacotron: Non-Autoregressive and Controllable TTS (2020)
- BVAE-TTS: Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech (ICLR 2021)
- LightSpeech: LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search (ICASSP 2021)
- Parallel Tacotron 2: Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling (2021)
- Grad-TTS: Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech (ICML 2021)
- VITS (flow based): Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech (ICML 2021)
- RAD-TTS: RAD-TTS: Parallel Flow-Based TTS with Robust Alignment Learning and Diverse Synthesis (ICML 2021 Workshop)
- WaveGrad 2: WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis (Interspeech 2021)
- PortaSpeech: PortaSpeech: Portable and High-Quality Generative Text-to-Speech (NeurIPS 2021)
- DelightfulTTS (To synthesize natural and high-quality speech from text): DelightfulTTS: The Microsoft Speech Synthesis System for Blizzard Challenge 2021 (Blizzard Challenge 2021)
- DiffGAN-TTS: DiffGAN-TTS: High-Fidelity and Efficient Text-to-Speech with Denoising Diffusion GANs (2022-01)
- BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis (ICLR 2022)
- JETS: JETS: Jointly Training FastSpeech2 and HiFi-GAN for End to End Text to Speech (Interspeech 2022)
- WavThruVec: WavThruVec: Latent speech representation as intermediate features for neural speech synthesis (2022-03)
- FastDiff: FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis (IJCAI 2022)
- NaturalSpeech: NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality (2022-05)
- DelightfulTTS 2: DelightfulTTS 2: End-to-End Speech Synthesis with Adversarial Vector-Quantized Auto-Encoders (Interspeech 2022)
- CLONE: Controllable and Lossless Non-Autoregressive End-to-End Text-to-Speech (2022-07)
- ZET-Speech: ZET-Speech: Zero-shot adaptive Emotion-controllable Text-to-Speech Synthesis with Diffusion and Style-based Models (Interspeech 2023)
Alignment Study
- Monotonic Attentionโ : Online and Linear-Time Attention by Enforcing Monotonic Alignments (ICML 2017)
- Monotonic Chunkwise Attentionโ : Monotonic Chunkwise Attention (ICLR 2018)
- Forward Attention in Sequence-to-sequence Acoustic Modelling for Speech Synthesis (ICASSP 2018)
- RNN-T for TTS: Initial investigation of an encoder-decoder end-to-end TTS framework using marginalization of monotonic hard latent alignments (2019)
- Location-Relative Attention Mechanisms For Robust Long-Form Speech Synthesis (ICASSP 2020)
- Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling (under review ICLR 2021)
- EfficientTTS: EfficientTTS: An Efficient and High-Quality Text-to-Speech Architecture (2020-12)
- VAENAR-TTS: VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis (2021-07)
- One TTS Alignment To Rule Them All (2021-08)
Data Efficiency
- Semi-Supervised Training for Improving Data Efficiency in End-to-End Speech Synthesis (2018)
- Almost Unsupervised Text to Speech and Automatic Speech Recognition (ICML 2019)
- Unsupervised Learning For Sequence-to-sequence Text-to-speech For Low-resource Languages (Interspeech 2020)
- Multilingual Speech Synthesis: One Model, Many Languages: Meta-learning for Multilingual Text-to-Speech (Interspeech 2020)
- Low-resource expressive text-to-speech using data augmentation (2020-11)
- One TTS Alignment To Rule Them All (2021-08)
- DenoiSpeech: DenoiSpeech: Denoising Text to Speech with Frame-Level Noise Modeling (ICASSP 2021)
- Revisiting Over-Smoothness in Text to Speech (ACL 2022)
- Unsupervised Text-to-Speech Synthesis by Unsupervised Automatic Speech Recognition (2022-03)
- Simple and Effective Unsupervised Speech Synthesis (2022-04)
- A Multi-Stage Multi-Codebook VQ-VAE Approach to High-Performance Neural TTS (Interspeech 2022)
- EPIC TTS Models (research on pruning): EPIC TTS Models: Empirical Pruning Investigations Characterizing Text-To-Speech Models (Interspeech 2022)
Vocoder
Autoregressive Model
- WaveNetโ : WaveNet: A Generative Model for Raw Audio (2016)
- WaveRNNโ : Efficient Neural Audio Synthesis (ICML 2018)
- WaveGANโ : Adversarial Audio Synthesis (ICLR 2019)
- LPCNetโ : LPCNet: Improving Neural Speech Synthesis Through Linear Prediction (ICASSP 2019)
- Towards achieving robust universal neural vocoding (Interspeech 2019)
- GAN-TTS: High Fidelity Speech Synthesis with Adversarial Networks (2019)
- MultiBand-WaveRNN: DurIAN: Duration Informed Attention Network For Multimodal Synthesis (2019)
- Chunked Autoregressive GAN for Conditional Waveform Synthesis (2021-10)
- Improved LPCNet: Neural Speech Synthesis on a Shoestring: Improving the Efficiency of LPCNet (ICASSP 2022)
- Bunched LPCNet2: Bunched LPCNet2: Efficient Neural Vocoders Covering Devices from Cloud to Edge (2022-03)
Non-Autoregressive Model
- Parallel-WaveNetโ : Parallel WaveNet: Fast High-Fidelity Speech Synthesis (2017)
- WaveGlowโ : WaveGlow: A Flow-based Generative Network for Speech Synthesis (2018)
- Parallel-WaveGANโ : Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram (2019)
- MelGANโ : MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis (NeurIPS 2019)
- MultiBand-MelGAN: Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech (2020)
- VocGAN: VocGAN: A High-Fidelity Real-time Vocoder with a Hierarchically-nested Adversarial Network (Interspeech 2020)
- WaveGrad: WaveGrad: Estimating Gradients for Waveform Generation (2020)
- DiffWave: DiffWave: A Versatile Diffusion Model for Audio Synthesis (2020)
- HiFi-GAN: HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis (NeurIPS 2020)
- Parallel-WaveGAN (New): Parallel waveform synthesis based on generative adversarial networks with voicing-aware conditional discriminators (2020-10)
- StyleMelGAN: StyleMelGAN: An Efficient High-Fidelity Adversarial Vocoder with Temporal Adaptive Normalization (ICASSP 2021)
- Improved parallel WaveGAN vocoder with perceptually weighted spectrogram loss (SLT 2021)
- Fre-GAN: Fre-GAN: Adversarial Frequency-consistent Audio Synthesis (Interspeech 2021)
- UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation (2021-07)
- iSTFTNet: iSTFTNet: Fast and Lightweight Mel-Spectrogram Vocoder Incorporating Inverse Short-Time Fourier Transform (ICASSP 2022)
- Parallel Synthesis for Autoregressive Speech Generation (2022-04)
- Avocodo: Avocodo: Generative Adversarial Network for Artifact-free Vocoder (2022-06)
Others
- (Robust vocoder): Towards Robust Neural Vocoding for Speech Generation: A Survey (2019)
- (Source-filter model based): Neural source-filter waveform models for statistical parametric speech synthesis (TASLP 2019)
- NHV: Neural Homomorphic Vocoder (Interspeech 2020)
- Universal MelGAN: Universal MelGAN: A Robust Neural Vocoder for High-Fidelity Waveform Generation in Multiple Domains (2020)
- Binaural Speech Synthesis: Neural Synthesis of Binaural Speech From Mono Audio (ICLR 2021)
- Checkerboard artifacts in neural vocoder: Upsampling artifacts in neural audio synthesis (ICASSP 2021)
- Universal Vocoder Based on Parallel WaveNet: Universal Neural Vocoding with Parallel WaveNet (ICASSP 2021)
- (Comparison of discriminator): GAN Vocoder: Multi-Resolution Discriminator Is All You Need (2021-03)
- Vocoder Benchmark: VocBench: A Neural Vocoder Benchmark for Speech Synthesis (2021-12)
- BigVGAN (Universal vocoder): BigVGAN: A Universal Neural Vocoder with Large-Scale Training (2022-06)
TTS towards Stylization
Expressive TTS
- ReferenceEncoder-Tacotronโ : Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron (ICML 2018)
- GST-Tacotronโ : Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis (ICML 2018)
- Predicting Expressive Speaking Style From Text In End-To-End Speech Synthesis (2018)
- GMVAE-Tacotron2โ : Hierarchical Generative Modeling for Controllable Speech Synthesis (ICLR 2019)
- BERT-TTS: Towards Transfer Learning for End-to-End Speech Synthesis from Deep Pre-Trained Language Models (2019)
- (Multi-style Decouple): Multi-Reference Neural TTS Stylization with Adversarial Cycle Consistency (2019)
- (Multi-style Decouple): Multi-reference Tacotron by Intercross Training for Style Disentangling,Transfer and Control in Speech Synthesis (Interspeech 2019)
- Mellotron: Mellotron: Multispeaker expressive voice synthesis by conditioning on rhythm, pitch and global style tokens (2019)
- Robust and fine-grained prosody control of end-to-end speech synthesis (ICASSP 2019)
- Flowtron (flow based): Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis (2020)
- (local style): Fully-hierarchical fine-grained prosody modeling for interpretable speech synthesis (ICASSP 2020)
- Controllable Neural Prosody Synthesis (Interspeech 2020)
- GraphSpeech: GraphSpeech: Syntax-Aware Graph Attention Network For Neural Speech Synthesis (2020-10)
- BERT-TTS: Improving Prosody Modelling with Cross-Utterance BERT Embeddings for End-to-end Speech Synthesis (2020-11)
- (Global Emotion Style Control): Controllable Emotion Transfer For End-to-End Speech Synthesis (2020-11)
- (Phone Level Style Control): Fine-grained Emotion Strength Transfer, Control and Prediction for Emotional Speech Synthesis (2020-11)
- (Phone Level Prosody Modelling): Mixture Density Network for Phone-Level Prosody Modelling in Speech Synthesis (ICASSP 2021)
- (Phone Level Prosody Modelling): Prosodic Clustering for Phoneme-level Prosody Control in End-to-End Speech Synthesis (ICASSP 2021)
- PeriodNet: PeriodNet: A non-autoregressive waveform generation model with a structure separating periodic and aperiodic components (ICASSP 2021)
- PnG BERT: PnG BERT: Augmented BERT on Phonemes and Graphemes for Neural TTS (Interspeech 2021)
- Towards Multi-Scale Style Control for Expressive Speech Synthesis (2021-04)
- Learning Robust Latent Representations for Controllable Speech Synthesis (2021-05)
- Diverse and Controllable Speech Synthesis with GMM-Based Phone-Level Prosody Modelling (2021-05)
- Improving Performance of Seen and Unseen Speech Style Transfer in End-to-end Neural TTS (2021-06)
- (Conversational Speech Synthesis): Controllable Context-aware Conversational Speech Synthesis (Interspeech 2021)
- DeepRapper: DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling (ACL 2021)
- Referee: Referee: Towards reference-free cross-speaker style transfer with low-quality data for expressive speech synthesis (2021)
- (Text-Based Insertion TTS): Zero-Shot Text-to-Speech for Text-Based Insertion in Audio Narration (Interspeech 2021)
- On the Interplay Between Sparsity, Naturalness, Intelligibility, and Prosody in Speech Synthesis (2021-10)
- Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models (2021-10)
- TTS for dubbing: Neural Dubber: Dubbing for Videos According to Scripts (NeurIPS 2021)
- Word-Level Style Control for Expressive, Non-attentive Speech Synthesis (SPECOM 2021)
- MsEmoTTS: MsEmoTTS: Multi-scale emotion transfer, prediction, and control for emotional speech synthesis (2022-01)
- Disentangling Style and Speaker Attributes for TTS Style Transfer (2022-01)
- Word-level prosody modeling: Unsupervised word-level prosody tagging for controllable speech synthesis (ICASSP 2022)
- ProsoSpeech: ProsoSpeech: Enhancing Prosody With Quantized Vector Pre-training in Text-to-Speech (ICASSP 2022)
- CampNet (speech editing):CampNet: Context-Aware Mask Prediction for End-to-End Text-Based Speech Editing (2022-02)
- vTTS (visual text): vTTS: visual-text to speech (2022-03)
- CopyCat2: CopyCat2: A Single Model for Multi-Speaker TTS and Many-to-Many Fine-Grained Prosody Transfer (Interspeech 2022)
- Prosody Cloning in Zero-Shot Multispeaker Text-to-Speech (Interspeech 2022)
- Expressive, Variable, and Controllable Duration Modelling in TTS (Interspeech 2022)
MultiSpeaker TTS
- Meta-Learning for TTSโ : Sample Efficient Adaptive Text-to-Speech (ICLR 2019)
- SV-Tacotronโ : Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis (NeurIPS 2018)
- Deep Voice V3โ : Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning (ICLR 2018)
- Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings (ICASSP 2020)
- MultiSpeech: MultiSpeech: Multi-Speaker Text to Speech with Transformer (2020)
- SC-WaveRNN: Speaker Conditional WaveRNN: Towards Universal Neural Vocoder for Unseen Speaker and Recording Conditions (Interspeech 2020)
- MultiSpeaker Dataset: AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselines (2020)
- Life-long learning for multi-speaker TTS: Continual Speaker Adaptation for Text-to-Speech Synthesis (2021-03)
- Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation (ICML 2021)
- Effective and Differentiated Use of Control Information for Multi-speaker Speech Synthesis (Interspeech 2021)
- Speaker Generation (2021-11)
- Meta-Voice: Meta-Voice: Fast few-shot style transfer for expressive voice cloning using meta learning (2021-11)
New Perspective on TTS
- PromptTTS: PromptTTS: Controllable Text-to-Speech with Text Descriptions (2022-11)
- VALL-E: Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers (2023-01)
- InstructTTS: InstructTTS: Modelling Expressive TTS in Discrete Latent Space with Natural Language Style Prompt (2023-01)
- Spear-TTS: Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision (2023-02)
- FoundationTTS: FoundationTTS: Text-to-Speech for ASR Customization with Generative Language Model (2023-03)
Voice Conversion
ASR & TTS Based
- (introduce PPG into voice conversion): Phonetic posteriorgrams for many-to-one voice conversion without parallel data training (2016)
- A Vocoder-free WaveNet Voice Conversion with Non-Parallel Data (2019)
- TTS-Skins: TTS Skins: Speaker Conversion via ASR (2019)
- Non-Parallel Sequence-to-Sequence Voice Conversion with Disentangled Linguistic and Speaker Representations (IEEE/ACM TASLP 2019)
- One-shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalization (Interspeech 2019)
- Cotatron (combine text information with voice conversion system): Cotatron: Transcription-Guided Speech Encoder for Any-to-Many Voice Conversion without Parallel Data (Interspeech 2020)
- (TTS & ASR): Voice Conversion by Cascading Automatic Speech Recognition and Text-to-Speech Synthesis with Prosody Transfer (Interspeech 2020)
- FragmentVC (wav to vec): FragmentVC: Any-to-Any Voice Conversion by End-to-End Extracting and Fusing Fine-Grained Voice Fragments With Attention (2020)
- Towards Natural and Controllable Cross-Lingual Voice Conversion Based on Neural TTS Model and Phonetic Posteriorgram (ICASSP 2021)
- (TTS & ASR): On Prosody Modeling for ASR+TTS based Voice Conversion (2021-07)
- Cloning one's voice using very limited data in the wild (2021-10)
VAE & Auto-Encoder Based
- VAE-VC (VAE based): Voice Conversion from Non-parallel Corpora Using Variational Auto-encoder (2016)
- (Speech representation learning by VQ-VAE): Unsupervised speech representation learning using WaveNet autoencoders (2019)
- Blow (Flow based): Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion (NeurIPS 2019)
- AutoVC: AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss (2019)
- F0-AutoVC: F0-consistent many-to-many non-parallel voice conversion via conditional autoencoder (ICASSP 2020)
- One-Shot Voice Conversion by Vector Quantization (ICASSP 2020)
- SpeechSplit (auto-encoder): Unsupervised Speech Decomposition via Triple Information Bottleneck (ICML 2020)
- NANSY: Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations (NeurIPS 2021)
GAN Based
- CycleGAN-VC V1: Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks (2017)
- StarGAN-VC: StarGAN-VC: non-parallel many-to-many Voice Conversion Using Star Generative Adversarial Networks (2018)
- CycleGAN-VC V2: CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion (2019)
- CycleGAN-VC V3: CycleGAN-VC3: Examining and Improving CycleGAN-VCs for Mel-spectrogram Conversion (2020)
- MaskCycleGAN-VC: MaskCycleGAN-VC: Learning Non-parallel Voice Conversion with Filling in Frames (ICASSP 2021)
Singing
Singing Voice Synthesis
- XiaoIce Band: XiaoIce Band: A Melody and Arrangement Generation Framework for Pop Music (KDD 2018)
- Mellotron: Mellotron: Multispeaker expressive voice synthesis by conditioning on rhythm, pitch and global style tokens (2019)
- ByteSing: ByteSing: A Chinese Singing Voice Synthesis System Using Duration Allocated Encoder-Decoder Acoustic Models and WaveRNN Vocoders (2020)
- JukeBox: Jukebox: A Generative Model for Music (2020)
- XiaoIce Sing: XiaoiceSing: A High-Quality and Integrated Singing Voice Synthesis System (2020)
- HiFiSinger: HiFiSinger: Towards High-Fidelity Neural Singing Voice Synthesis (2019)
- Sequence-to-sequence Singing Voice Synthesis with Perceptual Entropy Loss (2020)
- Learn2Sing: Learn2Sing: Target Speaker Singing Voice Synthesis by learning from a Singing Teacher (2020-11)
- MusicBERT: MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training (ACL 2021)
- SingGAN (Singing Voice Vocoder): SingGAN: Generative Adversarial Network For High-Fidelity Singing Voice Generation (AAAI 2022)
- Background music generation: Video Background Music Generation with Controllable Music Transformer (ACM Multimedia 2021)
- Multi-Singer (Singing Voice Vocoder): Multi-Singer: Fast Multi-Singer Singing Voice Vocoder With A Large-Scale Corpus (ACM Multimedia 2021)
- Rapping-singing voice synthesis: Rapping-Singing Voice Synthesis based on Phoneme-level Prosody Control (SSW 11)
- VISinger (VIST for Singing Voice Synthesis): VISinger: Variational Inference with Adversarial Learning for End-to-End Singing Voice Synthesis (2021-10)
- Opencpop: Opencpop: A High-Quality Open Source Chinese Popular Song Corpus for Singing Voice Synthesis (2022-01)
- Learning the Beauty in Songs: Neural Singing Voice Beautifier (ACL 2022)
- Learn2Sing 2.0: Diffusion and Mutual Information-Based Target Speaker SVS by Learning from Singing Teacher (2022-03)
- MusicLM: MusicLM: Generating Music From Text (2023-01)
- SingSong: SingSong: Generating musical accompaniments from singing (2023-01)
Singing Voice Conversion
- A Universal Music Translation Network (2018)
- Unsupervised Singing Voice Conversion (Interspeech 2019)
- PitchNet: PitchNet: Unsupervised Singing Voice Conversion with Pitch Adversarial Network (ICASSP 2020)
- DurIAN-SC: DurIAN-SC: Duration Informed Attention Network based Singing Voice Conversion System (Interspeech 2020)
- Speech-to-Singing Conversion based on Boundary Equilibrium GAN (Interspeech 2020)
- PPG-based singing voice conversion with adversarial representation learning (2020)