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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
Multi-Singer: Fast Multi-Singer Singing Voice Vocoder With A Large-Scale CorpusCode1
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyoneCode1
Fine-grained style control in Transformer-based Text-to-speech SynthesisCode1
EdiTTS: Score-based Editing for Controllable Text-to-SpeechCode1
WaveGrad 2: Iterative Refinement for Text-to-Speech SynthesisCode1
RyanSpeech: A Corpus for Conversational Text-to-Speech SynthesisCode1
Enhancing Speaking Styles in Conversational Text-to-Speech Synthesis with Graph-based Multi-modal Context ModelingCode1
RAD-TTS: Parallel Flow-Based TTS with Robust Alignment Learning and Diverse SynthesisCode1
Grad-TTS: A Diffusion Probabilistic Model for Text-to-SpeechCode1
KazakhTTS: An Open-Source Kazakh Text-to-Speech Synthesis DatasetCode1
Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph EntitiesCode1
Wave-Tacotron: Spectrogram-free end-to-end text-to-speech synthesisCode1
Semi-supervised URL Segmentation with Recurrent Neural NetworksPre-trained on Knowledge Graph EntitiesCode1
Effective Deep Learning Models for Automatic Diacritization of Arabic TextCode1
WaveGrad: Estimating Gradients for Waveform GenerationCode1
Enhancing Speech Intelligibility in Text-To-Speech Synthesis using Speaking Style ConversionCode1
FastSpeech 2: Fast and High-Quality End-to-End Text to SpeechCode1
End-to-End Adversarial Text-to-SpeechCode1
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment SearchCode1
Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech SynthesisCode1
In Other News: A Bi-style Text-to-speech Model for Synthesizing Newscaster Voice with Limited DataCode1
Visualization and Interpretation of Latent Spaces for Controlling Expressive Speech Synthesis through Audio AnalysisCode1
Exploring Transfer Learning for Low Resource Emotional TTSCode1
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech SynthesisCode1
Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided AttentionCode1
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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