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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 251–275 of 332 papers

TitleStatusHype
Russian Stress Prediction using Maximum Entropy Ranking—0
Zero-Shot Streaming Text to Speech Synthesis with Transducer and Auto-Regressive Modeling—0
Zero-shot text-to-speech synthesis conditioned using self-supervised speech representation model—0
S2ST-Omni: An Efficient and Scalable Multilingual Speech-to-Speech Translation Framework via Seamless Speech-Text Alignment and Streaming Speech Generation—0
SALF-MOS: Speaker Agnostic Latent Features Downsampled for MOS Prediction—0
SALTTS: Leveraging Self-Supervised Speech Representations for improved Text-to-Speech Synthesis—0
Alternate Endings: Improving Prosody for Incremental Neural TTS with Predicted Future Text Input—0
Schrodinger Bridges Beat Diffusion Models on Text-to-Speech Synthesis—0
Semi-supervised Learning for Multi-speaker Text-to-speech Synthesis Using Discrete Speech Representation—0
Listening while Speaking and Visualizing: Improving ASR through Multimodal Chain—0
ZET-Speech: Zero-shot adaptive Emotion-controllable Text-to-Speech Synthesis with Diffusion and Style-based Models—0
Shallow Flow Matching for Coarse-to-Fine Text-to-Speech Synthesis—0
Simultaneous Speech-to-Speech Translation System with Neural Incremental ASR, MT, and TTS—0
SLMGAN: Exploiting Speech Language Model Representations for Unsupervised Zero-Shot Voice Conversion in GANs—0
SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis—0
Speaker-independent raw waveform model for glottal excitation—0
Speaker verification-derived loss and data augmentation for DNN-based multispeaker speech synthesis—0
Aligning Opinions: Cross-Lingual Opinion Mining with Dependencies—0
Speaking style adaptation in Text-To-Speech synthesis using Sequence-to-sequence models with attention—0
Speech Bandwidth Expansion Via High Fidelity Generative Adversarial Networks—0
Speech denoising by parametric resynthesis—0
WebWOZ: A Platform for Designing and Conducting Web-based Wizard of Oz Experiments—0
Spontaneous Style Text-to-Speech Synthesis with Controllable Spontaneous Behaviors Based on Language Models—0
A distributed cloud-based dialog system for conversational application development—0
Stable-TTS: Stable Speaker-Adaptive Text-to-Speech Synthesis via Prosody Prompting—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