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 101–150 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
Phonetic Enhanced Language Modeling for Text-to-Speech Synthesis—0
Enhancing Zero-shot Text-to-Speech Synthesis with Human Feedback—0
DLPO: Diffusion Model Loss-Guided Reinforcement Learning for Fine-Tuning Text-to-Speech Diffusion Models—0
Evaluating Text-to-Speech Synthesis from a Large Discrete Token-based Speech Language Model—0
RALL-E: Robust Codec Language Modeling with Chain-of-Thought Prompting for Text-to-Speech Synthesis—0
PSCodec: A Series of High-Fidelity Low-bitrate Neural Speech Codecs Leveraging Prompt Encoders—0
Bayesian Parameter-Efficient Fine-Tuning for Overcoming Catastrophic ForgettingCode0
Noise-robust zero-shot text-to-speech synthesis conditioned on self-supervised speech-representation model with adapters—0
Boosting Large Language Model for Speech Synthesis: An Empirical Study—0
Normalization of Lithuanian Text Using Regular Expressions—0
MM-TTS: Multi-modal Prompt based Style Transfer for Expressive Text-to-Speech Synthesis—0
An Experimental Study: Assessing the Combined Framework of WavLM and BEST-RQ for Text-to-Speech Synthesis—0
Schrodinger Bridges Beat Diffusion Models on Text-to-Speech Synthesis—0
Code-Mixed Text to Speech Synthesis under Low-Resource Constraints—0
Guided Flows for Generative Modeling and Decision Making—0
Generative Pre-training for Speech with Flow Matching—0
Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition ErrorsCode0
Attentive Multi-Layer Perceptron for Non-autoregressive GenerationCode0
Unified speech and gesture synthesis using flow matching—0
The VoiceMOS Challenge 2023: Zero-shot Subjective Speech Quality Prediction for Multiple Domains—0
DurIAN-E: Duration Informed Attention Network For Expressive Text-to-Speech Synthesis—0
The FruitShell French synthesis system at the Blizzard 2023 Challenge—0
Towards Spontaneous Style Modeling with Semi-supervised Pre-training for Conversational Text-to-Speech Synthesis—0
SALTTS: Leveraging Self-Supervised Speech Representations for improved Text-to-Speech Synthesis—0
Comparing normalizing flows and diffusion models for prosody and acoustic modelling in text-to-speech—0
Show:102550
← PrevPage 3 of 7Next →
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