SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–225 of 332 papers

TitleStatusHype
Towards Lifelong Learning of Multilingual Text-To-Speech SynthesisCode0
Environment Aware Text-to-Speech Synthesis—0
Prosody-TTS: An end-to-end speech synthesis system with prosody control—0
Neural Speech Synthesis in German—0
Guided-TTS:Text-to-Speech with Untranscribed Speech—0
Conditioning Sequence-to-sequence Networks with Learned Activations—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
PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior—0
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
Dual Script E2E framework for Multilingual and Code-Switching ASR—0
Phrase break prediction with bidirectional encoder representations in Japanese text-to-speech synthesisCode0
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
Alternate Endings: Improving Prosody for Incremental Neural TTS with Predicted Future Text Input—0
VARA-TTS: Non-Autoregressive Text-to-Speech Synthesis based on Very Deep VAE with Residual Attention—0
Voice Cloning: a Multi-Speaker Text-to-Speech Synthesis Approach based on Transfer Learning—0
Triple M: A Practical Text-to-speech Synthesis System With Multi-guidance Attention And Multi-band Multi-time LPCNet—0
Show:102550
← PrevPage 9 of 14Next →
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