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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 51–75 of 332 papers

TitleStatusHype
UMETTS: A Unified Framework for Emotional Text-to-Speech Synthesis with Multimodal PromptsCode1
MnTTS2: An Open-Source Multi-Speaker Mongolian Text-to-Speech Synthesis DatasetCode1
In Other News: A Bi-style Text-to-speech Model for Synthesizing Newscaster Voice with Limited DataCode1
Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph EntitiesCode1
Enhancing Speaking Styles in Conversational Text-to-Speech Synthesis with Graph-based Multi-modal Context ModelingCode1
Grad-TTS: A Diffusion Probabilistic Model for Text-to-SpeechCode1
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment SearchCode1
Imaginary Voice: Face-styled Diffusion Model for Text-to-SpeechCode1
QS-TTS: Towards Semi-Supervised Text-to-Speech Synthesis via Vector-Quantized Self-Supervised Speech Representation LearningCode1
ArTST: Arabic Text and Speech TransformerCode1
Improved Child Text-to-Speech Synthesis through Fastpitch-based Transfer LearningCode1
Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech SynthesisCode1
Fine-grained style control in Transformer-based Text-to-speech SynthesisCode1
FastSpeech 2: Fast and High-Quality End-to-End Text to SpeechCode1
Automatic Prosody Annotation with Pre-Trained Text-Speech ModelCode1
End-to-End Adversarial Text-to-SpeechCode1
Enhancing Speech Intelligibility in Text-To-Speech Synthesis using Speaking Style ConversionCode1
Exploring Transfer Learning for Low Resource Emotional TTSCode1
Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided AttentionCode1
Effective Deep Learning Models for Automatic Diacritization of Arabic TextCode1
EdiTTS: Score-based Editing for Controllable Text-to-SpeechCode1
Accented Text-to-Speech Synthesis with a Conditional Variational AutoencoderCode1
Learning Arousal-Valence Representation from Categorical Emotion Labels of SpeechCode1
RyanSpeech: A Corpus for Conversational Text-to-Speech SynthesisCode1
Semi-supervised URL Segmentation with Recurrent Neural NetworksPre-trained on Knowledge Graph EntitiesCode1
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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