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Text-To-Speech Synthesis

Text-To-Speech Synthesis is a machine learning task that involves converting written text into spoken words. The goal is to generate synthetic speech that sounds natural and resembles human speech as closely as possible.

Papers

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Showing 151–200 of 332 papers

TitleStatusHype
FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech SynthesisCode2
The PartialSpoof Database and Countermeasures for the Detection of Short Fake Speech Segments Embedded in an Utterance—0
SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis—0
VQTTS: High-Fidelity Text-to-Speech Synthesis with Self-Supervised VQ Acoustic Feature—0
Unsupervised Text-to-Speech Synthesis by Unsupervised Automatic Speech RecognitionCode1
Applying Syntaxx2013Prosody Mapping Hypothesis and Prosodic Well-Formedness Constraints to Neural Sequence-to-Sequence Speech Synthesis—0
AutoTTS: End-to-End Text-to-Speech Synthesis through Differentiable Duration Modeling—0
ECAPA-TDNN for Multi-speaker Text-to-speech SynthesisCode0
Text-free non-parallel many-to-many voice conversion using normalising flows—0
iSTFTNet: Fast and Lightweight Mel-Spectrogram Vocoder Incorporating Inverse Short-Time Fourier TransformCode2
Generative Modeling for Low Dimensional Speech Attributes with Neural Spline FlowsCode2
Deep Performer: Score-to-Audio Music Performance Synthesis—0
Multi-Stage Deep Transfer Learning for EmIoT-enabled Human-Computer Interaction—0
Transformer-based Models of Text Normalization for Speech Applications—0
Multi-speaker Multi-style Text-to-speech Synthesis With Single-speaker Single-style Training Data Scenarios—0
Multi-Singer: Fast Multi-Singer Singing Voice Vocoder With A Large-Scale CorpusCode1
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyoneCode1
Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance—0
Systematic Inequalities in Language Technology Performance across the World's LanguagesCode0
Fine-grained style control in Transformer-based Text-to-speech SynthesisCode1
Towards Lifelong Learning of Multilingual Text-To-Speech SynthesisCode0
Environment Aware Text-to-Speech Synthesis—0
EdiTTS: Score-based Editing for Controllable Text-to-SpeechCode1
Prosody-TTS: An end-to-end speech synthesis system with prosody control—0
Neural Speech Synthesis in German—0
PortaSpeech: Portable and High-Quality Generative Text-to-SpeechCode2
Conditioning Sequence-to-sequence Networks with Learned Activations—0
Guided-TTS:Text-to-Speech with Untranscribed Speech—0
Low-Latency Incremental Text-to-Speech Synthesis with Distilled Context Prediction Network—0
A Unified Transformer-based Framework for Duplex Text Normalization—0
Extending Text-to-Speech Synthesis with Articulatory Movement Prediction using Ultrasound Tongue ImagingCode0
Location, Location: Enhancing the Evaluation of Text-to-Speech Synthesis Using the Rapid Prosody Transcription Paradigm—0
Speech Synthesis from Text and Ultrasound Tongue Image-based Articulatory InputCode0
WaveGrad 2: Iterative Refinement for Text-to-Speech SynthesisCode1
RyanSpeech: A Corpus for Conversational Text-to-Speech SynthesisCode1
PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior—0
Enhancing Speaking Styles in Conversational Text-to-Speech Synthesis with Graph-based Multi-modal Context ModelingCode1
An objective evaluation of the effects of recording conditions and speaker characteristics in multi-speaker deep neural speech synthesis—0
Speaker verification-derived loss and data augmentation for DNN-based multispeaker speech synthesis—0
RAD-TTS: Parallel Flow-Based TTS with Robust Alignment Learning and Diverse SynthesisCode1
Dual Script E2E framework for Multilingual and Code-Switching ASR—0
Grad-TTS: A Diffusion Probabilistic Model for Text-to-SpeechCode1
DiffSinger: Singing Voice Synthesis via Shallow Diffusion MechanismCode2
Phrase break prediction with bidirectional encoder representations in Japanese text-to-speech synthesisCode0
KazakhTTS: An Open-Source Kazakh Text-to-Speech Synthesis DatasetCode1
Enhancing Word-Level Semantic Representation via Dependency Structure for Expressive Text-to-Speech Synthesis—0
Flavored Tacotron: Conditional Learning for Prosodic-linguistic Features—0
Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability—0
PnG BERT: Augmented BERT on Phonemes and Graphemes for Neural TTS—0
Continual Speaker Adaptation for Text-to-Speech Synthesis—0
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All datasetsLJSpeech20000 utterancesCMUDict 0.7bHUI speech corpusThorsten voice 21.02 neutralTrinity Speech-Gesture Dataset

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NaturalSpeechAudio Quality MOS4.56—Unverified
2VITSAudio Quality MOS4.43—Unverified
3Grad-TTS + HiFiGAN (1000 steps)Audio Quality MOS4.37—Unverified
4FastSpeech 2 + HiFiGANAudio Quality MOS4.34—Unverified
5Glow-TTS + HiFiGANAudio Quality MOS4.34—Unverified
6FastSpeech 2 + HiFiGANAudio Quality MOS4.32—Unverified
7FastDiff (4 steps)Audio Quality MOS4.28—Unverified
8FastDiff-TTSAudio Quality MOS4.03—Unverified
9Transformer TTS (Mel + WaveGlow)Audio Quality MOS3.88—Unverified
10FastSpeech (Mel + WaveGlow)Audio Quality MOS3.84—Unverified
#ModelMetricClaimedVerifiedStatus
1Mia10-keyword Speech Commands dataset16—Unverified
#ModelMetricClaimedVerifiedStatus
1Token-Level Ensemble DistillationPhoneme Error Rate4.6—Unverified
#ModelMetricClaimedVerifiedStatus
1Tacotron 2Mean Opinion Score3.74—Unverified
#ModelMetricClaimedVerifiedStatus
1Tacotron 2Mean Opinion Score3.49—Unverified
#ModelMetricClaimedVerifiedStatus
1Match-TTSGMOS3.7—Unverified