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 101–125 of 332 papers

TitleStatusHype
DurIAN-E 2: Duration Informed Attention Network with Adaptive Variational Autoencoder and Adversarial Learning for Expressive Text-to-Speech Synthesis—0
Efficient training strategies for natural sounding speech synthesis and speaker adaptation based on FastPitch—0
Bahasa Harmony: A Comprehensive Dataset for Bahasa Text-to-Speech Synthesis with Discrete Codec Modeling of EnGen-TTS—0
HALL-E: Hierarchical Neural Codec Language Model for Minute-Long Zero-Shot Text-to-Speech Synthesis—0
Generative Semantic Communication for Text-to-Speech Synthesis—0
Accent conversion using discrete units with parallel data synthesized from controllable accented TTS—0
StyleFusion TTS: Multimodal Style-control and Enhanced Feature Fusion for Zero-shot Text-to-speech Synthesis—0
StyleTTS-ZS: Efficient High-Quality Zero-Shot Text-to-Speech Synthesis with Distilled Time-Varying Style Diffusion—0
Text-To-Speech Synthesis In The Wild—0
Full-text Error Correction for Chinese Speech Recognition with Large Language Model—0
What happens to diffusion model likelihood when your model is conditional?—0
AS-Speech: Adaptive Style For Speech Synthesis—0
Speech Bandwidth Expansion Via High Fidelity Generative Adversarial Networks—0
Spontaneous Style Text-to-Speech Synthesis with Controllable Spontaneous Behaviors Based on Language Models—0
Autoregressive Speech Synthesis without Vector Quantization—0
Improving Accented Speech Recognition using Data Augmentation based on Unsupervised Text-to-Speech Synthesis—0
Robust Zero-Shot Text-to-Speech Synthesis with Reverse Inference Optimization—0
FLY-TTS: Fast, Lightweight and High-Quality End-to-End Text-to-Speech Synthesis—0
Multi-Scale Accent Modeling and Disentangling for Multi-Speaker Multi-Accent Text-to-Speech Synthesis—0
VALL-E R: Robust and Efficient Zero-Shot Text-to-Speech Synthesis via Monotonic Alignment—0
Meta Learning Text-to-Speech Synthesis in over 7000 LanguagesCode0
Autoregressive Diffusion Transformer for Text-to-Speech Synthesis—0
VALL-E 2: Neural Codec Language Models are Human Parity Zero-Shot Text to Speech Synthesizers—0
Improving Audio Codec-based Zero-Shot Text-to-Speech Synthesis with Multi-Modal Context and Large Language Model—0
Style Mixture of Experts for Expressive Text-To-Speech Synthesis—0
Show:102550
← PrevPage 5 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