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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 201–225 of 332 papers

TitleStatusHype
Alternate Endings: Improving Prosody for Incremental Neural TTS with Predicted Future Text Input—0
VARA-TTS: Non-Autoregressive Text-to-Speech Synthesis based on Very Deep VAE with Residual Attention—0
Voice Cloning: a Multi-Speaker Text-to-Speech Synthesis Approach based on Transfer Learning—0
Triple M: A Practical Text-to-speech Synthesis System With Multi-guidance Attention And Multi-band Multi-time LPCNet—0
Parallel WaveNet conditioned on VAE latent vectors—0
Using previous acoustic context to improve Text-to-Speech synthesis—0
Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph EntitiesCode1
Simultaneous Speech-to-Speech Translation System with Neural Incremental ASR, MT, and TTS—0
Fine-grained Style Modeling, Transfer and Prediction in Text-to-Speech Synthesis via Phone-Level Content-Style Disentanglement—0
Wave-Tacotron: Spectrogram-free end-to-end text-to-speech synthesisCode1
Semi-supervised URL Segmentation with Recurrent Neural NetworksPre-trained on Knowledge Graph EntitiesCode1
Augmenting Images for ASR and TTS through Single-loop and Dual-loop Multimodal Chain Framework—0
Incremental Machine Speech Chain Towards Enabling Listening while Speaking in Real-time—0
Effective Deep Learning Models for Automatic Diacritization of Arabic TextCode1
GraphSpeech: Syntax-Aware Graph Attention Network For Neural Speech Synthesis—0
An Investigation of the Relation Between Grapheme Embeddings and Pronunciation for Tacotron-based Systems—0
End-to-End Text-to-Speech using Latent Duration based on VQ-VAE—0
MIA-Prognosis: A Deep Learning Framework to Predict Therapy ResponseCode0
Automatic Arabic Dialect Identification Systems for Written Texts: A Survey—0
Hierarchical Multi-Grained Generative Model for Expressive Speech Synthesis—0
Controllable neural text-to-speech synthesis using intuitive prosodic features—0
What the Future Brings: Investigating the Impact of Lookahead for Incremental Neural TTS—0
Voice Conversion by Cascading Automatic Speech Recognition and Text-to-Speech Synthesis with Prosody Transfer—0
WaveGrad: Estimating Gradients for Waveform GenerationCode1
Enhancing Speech Intelligibility in Text-To-Speech Synthesis using Speaking Style ConversionCode1
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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