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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 101–125 of 332 papers

TitleStatusHype
AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms—0
EPIC TTS Models: Empirical Pruning Investigations Characterizing Text-To-Speech Models—0
Exploring Transfer Learning for Urdu Speech Synthesis—0
CASSANDRA: A multipurpose configurable voice-enabled human-computer-interface—0
Fast Bootstrapping of Grapheme to Phoneme System for Under-resourced Languages - Application to the Iban Language—0
Environment Aware Text-to-Speech Synthesis—0
Building Text-to-Speech Systems for Resource Poor Languages—0
Enhancing Zero-shot Text-to-Speech Synthesis with Human Feedback—0
Building a synchronous corpus of acoustic and 3D facial marker data for adaptive audio-visual speech synthesis—0
An In-depth Analysis of the Effect of Text Normalization in Social Media—0
Fine-grained Style Modeling, Transfer and Prediction in Text-to-Speech Synthesis via Phone-Level Content-Style Disentanglement—0
Flavored Tacotron: Conditional Learning for Prosodic-linguistic Features—0
Adaptive Parser-Centric Text Normalization—0
FLY-TTS: Fast, Lightweight and High-Quality End-to-End Text-to-Speech Synthesis—0
FMSD-TTS: Few-shot Multi-Speaker Multi-Dialect Text-to-Speech Synthesis for Ü-Tsang, Amdo and Kham Speech Dataset Generation—0
Full-text Error Correction for Chinese Speech Recognition with Large Language Model—0
Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance—0
Generative adversarial network-based glottal waveform model for statistical parametric speech synthesis—0
HALL-E: Hierarchical Neural Codec Language Model for Minute-Long Zero-Shot Text-to-Speech Synthesis—0
End-to-End Text-to-Speech using Latent Duration based on VQ-VAE—0
Generative Pre-training for Speech with Flow Matching—0
Generative Semantic Communication for Text-to-Speech Synthesis—0
BUCEADOR, a multi-language search engine for digital libraries—0
End-to-End Feedback Loss in Speech Chain Framework via Straight-Through Estimator—0
Boosting Large Language Model for Speech Synthesis: An Empirical Study—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