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 226–250 of 332 papers

TitleStatusHype
An Experimental Study: Assessing the Combined Framework of WavLM and BEST-RQ for Text-to-Speech Synthesis—0
Predicting Expressive Speaking Style From Text In End-To-End Speech Synthesis—0
Predicting Romanian Stress Assignment—0
Probing Speaker-specific Features in Speaker Representations—0
A Multi-Agent Framework for Automated Qinqiang Opera Script Generation Using Large Language Models—0
PROEMO: Prompt-Driven Text-to-Speech Synthesis Based on Emotion and Intensity Control—0
PSCodec: A Series of High-Fidelity Low-bitrate Neural Speech Codecs Leveraging Prompt Encoders—0
ProsodyFM: Unsupervised Phrasing and Intonation Control for Intelligible Speech Synthesis—0
Prosody-TTS: An end-to-end speech synthesis system with prosody control—0
Pseudo-Autoregressive Neural Codec Language Models for Efficient Zero-Shot Text-to-Speech Synthesis—0
Punjabi Text-To-Speech Synthesis System—0
Wasserstein GAN and Waveform Loss-based Acoustic Model Training for Multi-speaker Text-to-Speech Synthesis Systems Using a WaveNet Vocoder—0
Waveform generation for text-to-speech synthesis using pitch-synchronous multi-scale generative adversarial networks—0
RALL-E: Robust Codec Language Modeling with Chain-of-Thought Prompting for Text-to-Speech Synthesis—0
Real-time Incremental Speech-to-Speech Translation of Dialogs—0
ReCAB-VAE: Gumbel-Softmax Variational Inference Based on Analytic Divergence—0
Refer-iTTS: A System for Referring in Spoken Installments to Objects in Real-World Images—0
Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability—0
DLPO: Diffusion Model Loss-Guided Reinforcement Learning for Fine-Tuning Text-to-Speech Diffusion Models—0
ReVISE: Self-Supervised Speech Resynthesis with Visual Input for Universal and Generalized Speech Enhancement—0
ReVISE: Self-Supervised Speech Resynthesis With Visual Input for Universal and Generalized Speech Regeneration—0
Revival with Voice: Multi-modal Controllable Text-to-Speech Synthesis—0
R-MelNet: Reduced Mel-Spectral Modeling for Neural TTS—0
Robust Zero-Shot Text-to-Speech Synthesis with Reverse Inference Optimization—0
RSS-TOBI - A Prosodically Enhanced Romanian Speech Corpus—0
Show:102550
← PrevPage 10 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