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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 1–10 of 332 papers

TitleStatusHype
ZipVoice: Fast and High-Quality Zero-Shot Text-to-Speech with Flow MatchingCode4
S2ST-Omni: An Efficient and Scalable Multilingual Speech-to-Speech Translation Framework via Seamless Speech-Text Alignment and Streaming Speech Generation—0
A Novel Data Augmentation Approach for Automatic Speaking Assessment on Opinion Expressions—0
CapSpeech: Enabling Downstream Applications in Style-Captioned Text-to-Speech—0
SALF-MOS: Speaker Agnostic Latent Features Downsampled for MOS Prediction—0
Chain-of-Thought Training for Open E2E Spoken Dialogue Systems—0
Zero-Shot Streaming Text to Speech Synthesis with Transducer and Auto-Regressive Modeling—0
Revival with Voice: Multi-modal Controllable Text-to-Speech Synthesis—0
Audio Jailbreak: An Open Comprehensive Benchmark for Jailbreaking Large Audio-Language ModelsCode1
FMSD-TTS: Few-shot Multi-Speaker Multi-Dialect Text-to-Speech Synthesis for Ü-Tsang, Amdo and Kham Speech Dataset Generation—0
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All datasetsLJSpeech20000 utterancesCMUDict 0.7bHUI speech corpusThorsten voice 21.02 neutralTrinity Speech-Gesture Dataset

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Tacotron 2Mean Opinion Score3.49—Unverified