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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 151–175 of 332 papers

TitleStatusHype
Guided Flows for Generative Modeling and Decision Making—0
Guided-TTS:Text-to-Speech with Untranscribed Speech—0
Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance—0
HALL-E: Hierarchical Neural Codec Language Model for Minute-Long Zero-Shot Text-to-Speech Synthesis—0
Hierarchical Multi-Grained Generative Model for Expressive Speech Synthesis—0
Hierarchical Representation of Prosody for Statistical Speech Synthesis—0
High-Quality Automatic Voice Over with Accurate Alignment: Supervision through Self-Supervised Discrete Speech Units—0
Hippocratic Abbreviation Expansion—0
HMM-based Mandarin Singing Voice Synthesis Using Tailored Synthesis Units and Question Sets—0
UzbekTagger: The rule-based POS tagger for Uzbek language—0
VAKTA-SETU: A Speech-to-Speech Machine Translation Service in Select Indic Languages—0
Improving Accented Speech Recognition using Data Augmentation based on Unsupervised Text-to-Speech Synthesis—0
Improving Audio Codec-based Zero-Shot Text-to-Speech Synthesis with Multi-Modal Context and Large Language Model—0
Improving homograph disambiguation with supervised machine learning—0
Incremental Disentanglement for Environment-Aware Zero-Shot Text-to-Speech Synthesis—0
Incremental Machine Speech Chain Towards Enabling Listening while Speaking in Real-time—0
Incremental Text-to-Speech Synthesis with Prefix-to-Prefix Framework—0
Individuality-Preserving Spectrum Modification for Articulation Disorders Using Phone Selective Synthesis—0
VALL-E 2: Neural Codec Language Models are Human Parity Zero-Shot Text to Speech Synthesizers—0
Investigating Inter- and Intra-speaker Voice Conversion using Audiobooks—0
Investigation of Japanese PnG BERT language model in text-to-speech synthesis for pitch accent language—0
Investigation of learning abilities on linguistic features in sequence-to-sequence text-to-speech synthesis—0
ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech—0
VALL-E R: Robust and Efficient Zero-Shot Text-to-Speech Synthesis via Monotonic Alignment—0
VARA-TTS: Non-Autoregressive Text-to-Speech Synthesis based on Very Deep VAE with Residual Attention—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