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 251–275 of 332 papers

TitleStatusHype
A unified sequence-to-sequence front-end model for Mandarin text-to-speech synthesis—0
Incremental Text-to-Speech Synthesis with Prefix-to-Prefix Framework—0
Spoofing Speaker Verification Systems with Deep Multi-speaker Text-to-speech SynthesisCode0
Effect of choice of probability distribution, randomness, and search methods for alignment modeling in sequence-to-sequence text-to-speech synthesis using hard alignment—0
The Theory behind Controllable Expressive Speech Synthesis: a Cross-disciplinary Approach—0
Modular Meta-Learning with Shrinkage—0
Evaluating Long-form Text-to-Speech: Comparing the Ratings of Sentences and Paragraphs—0
Neural Harmonic-plus-Noise Waveform Model with Trainable Maximum Voice Frequency for Text-to-Speech Synthesis—0
MelNet: A Generative Model for Audio in the Frequency DomainCode0
Listening while Speaking and Visualizing: Improving ASR through Multimodal Chain—0
Neural Text Normalization with Subword Units—0
Neural Models of Text Normalization for Speech Applications—0
Non-Autoregressive Neural Text-to-SpeechCode0
Effective parameter estimation methods for an ExcitNet model in generative text-to-speech systemsCode0
Direct speech-to-speech translation with a sequence-to-sequence modelCode0
Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion—0
Speech denoising by parametric resynthesis—0
Generative adversarial network-based glottal waveform model for statistical parametric speech synthesis—0
AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms—0
End-to-End Feedback Loss in Speech Chain Framework via Straight-Through Estimator—0
Waveform generation for text-to-speech synthesis using pitch-synchronous multi-scale generative adversarial networks—0
Speaking style adaptation in Text-To-Speech synthesis using Sequence-to-sequence models with attention—0
Investigation of enhanced Tacotron text-to-speech synthesis systems with self-attention for pitch accent languageCode0
A Challenge Set and Methods for Noun-Verb Ambiguity—0
Predicting Expressive Speaking Style From Text In End-To-End Speech Synthesis—0
Show:102550
← PrevPage 11 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