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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 226–250 of 332 papers

TitleStatusHype
Parallel WaveNet conditioned on VAE latent vectors—0
Using previous acoustic context to improve Text-to-Speech synthesis—0
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
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
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
Multi-speaker Text-to-speech Synthesis Using Deep Gaussian Processes—0
Normalizing Text using Language Modelling based on Phonetics and String Similarity—0
Investigation of learning abilities on linguistic features in sequence-to-sequence text-to-speech synthesis—0
Semi-supervised Learning for Multi-speaker Text-to-speech Synthesis Using Discrete Speech Representation—0
Style Variation as a Vantage Point for Code-Switching—0
Neural Text-to-Speech Synthesis for an Under-Resourced Language in a Diglossic Environment: the Case of Gascon Occitan—0
Comparison of Speech Representations for Automatic Quality Estimation in Multi-Speaker Text-to-Speech SynthesisCode0
Using VAEs and Normalizing Flows for One-shot Text-To-Speech Synthesis of Expressive Speech—0
Cross-lingual Multi-speaker Text-to-speech Synthesis for Voice Cloning without Using Parallel Corpus for Unseen Speakers—0
Independent and automatic evaluation of acoustic-to-articulatory inversion modelsCode0
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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