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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

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Showing 176–200 of 332 papers

TitleStatusHype
Large tagset labeling using Feed Forward Neural Networks. Case study on Romanian Language—0
AS-Speech: Adaptive Style For Speech Synthesis—0
LDC Forced Aligner—0
Variations prosodiques en synth\`ese par s\'election d'unit\'es: l'exemple des phrases interrogatives (Prosodic variations in unit-based speech synthesis: the example of interrogative sentences) [in French]—0
Learning Sentiment Lexicons in Spanish—0
Leveraging supplemental representations for sequential transduction—0
Lightweight End-to-end Text-to-speech Synthesis for low resource on-device applications—0
A Review of Deep Learning Techniques for Speech Processing—0
Listening while Speaking: Speech Chain by Deep Learning—0
Location, Location: Enhancing the Evaluation of Text-to-Speech Synthesis Using the Rapid Prosody Transcription Paradigm—0
Low-Latency Incremental Text-to-Speech Synthesis with Distilled Context Prediction Network—0
Low-Resource Text-to-Speech Synthesis Using Noise-Augmented Training of ForwardTacotron—0
M2-CTTS: End-to-End Multi-scale Multi-modal Conversational Text-to-Speech Synthesis—0
Machine Speech Chain with One-shot Speaker Adaptation—0
Vers une annotation automatique de corpus audio pour la synth\`ese de parole (Towards Fully Automatic Annotation of Audio Books for Text-To-Speech (TTS) Synthesis) [in French]—0
Applying Syntaxx2013Prosody Mapping Hypothesis and Prosodic Well-Formedness Constraints to Neural Sequence-to-Sequence Speech Synthesis—0
Minimally Supervised Number Normalization—0
Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-To-Speech—0
MM-TTS: Multi-modal Prompt based Style Transfer for Expressive Text-to-Speech Synthesis—0
Accent conversion using discrete units with parallel data synthesized from controllable accented TTS—0
Modular Meta-Learning with Shrinkage—0
Applying Automated Machine Translation to Educational Video Courses—0
MParrotTTS: Multilingual Multi-speaker Text to Speech Synthesis in Low Resource Setting—0
Voice Cloning: a Multi-Speaker Text-to-Speech Synthesis Approach based on Transfer Learning—0
Multi-Scale Accent Modeling and Disentangling for Multi-Speaker Multi-Accent Text-to-Speech Synthesis—0
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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