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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 276–300 of 332 papers

TitleStatusHype
Dual Script E2E framework for Multilingual and Code-Switching ASR—0
StyleFusion TTS: Multimodal Style-control and Enhanced Feature Fusion for Zero-shot Text-to-speech Synthesis—0
Style Mixture of Experts for Expressive Text-To-Speech Synthesis—0
What happens to diffusion model likelihood when your model is conditional?—0
A Challenge Set and Methods for Noun-Verb Ambiguity—0
StyleTTS-ZS: Efficient High-Quality Zero-Shot Text-to-Speech Synthesis with Distilled Time-Varying Style Diffusion—0
Style Variation as a Vantage Point for Code-Switching—0
SynPaFlex-Corpus: An Expressive French Audiobooks Corpus dedicated to expressive speech synthesis.—0
What the Future Brings: Investigating the Impact of Lookahead for Incremental Neural TTS—0
Technology Pipeline for Large Scale Cross-Lingual Dubbing of Lecture Videos into Multiple Indian Languages—0
Text-free non-parallel many-to-many voice conversion using normalising flows—0
Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesis—0
Text Normalization and Unit Selection for a Memory Based Non Uniform Unit Selection TTS in Malayalam—0
Texto4Science: a Quebec French Database of Annotated Short Text Messages—0
Text-to-speech synthesis based on latent variable conversion using diffusion probabilistic model and variational autoencoder—0
Text-To-Speech Synthesis In The Wild—0
The FruitShell French synthesis system at the Blizzard 2023 Challenge—0
The PartialSpoof Database and Countermeasures for the Detection of Short Fake Speech Segments Embedded in an Utterance—0
The Theory behind Controllable Expressive Speech Synthesis: a Cross-disciplinary Approach—0
The VoiceMOS Challenge 2023: Zero-shot Subjective Speech Quality Prediction for Multiple Domains—0
Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion—0
Accented Text-to-Speech Synthesis with Limited Data—0
Towards Fully Automatic Annotation of Audio Books for TTS—0
AutoStyle-TTS: Retrieval-Augmented Generation based Automatic Style Matching Text-to-Speech Synthesis—0
Autoregressive Speech Synthesis without Vector Quantization—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