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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
SLMGAN: Exploiting Speech Language Model Representations for Unsupervised Zero-Shot Voice Conversion in GANs—0
High-Quality Automatic Voice Over with Accurate Alignment: Supervision through Self-Supervised Discrete Speech Units—0
Voicebox: Text-Guided Multilingual Universal Speech Generation at ScaleCode0
ZET-Speech: Zero-shot adaptive Emotion-controllable Text-to-Speech Synthesis with Diffusion and Style-based Models—0
VAKTA-SETU: A Speech-to-Speech Machine Translation Service in Select Indic Languages—0
MParrotTTS: Multilingual Multi-speaker Text to Speech Synthesis in Low Resource Setting—0
A unified front-end framework for English text-to-speech synthesis—0
Accented Text-to-Speech Synthesis with Limited Data—0
M2-CTTS: End-to-End Multi-scale Multi-modal Conversational Text-to-Speech Synthesis—0
A Review of Deep Learning Techniques for Speech Processing—0
Zero-shot text-to-speech synthesis conditioned using self-supervised speech representation model—0
Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesis—0
A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI—0
Controllable Prosody Generation With Partial Inputs—0
Do Prosody Transfer Models Transfer Prosody?—0
ParrotTTS: Text-to-Speech synthesis by exploiting self-supervised representations—0
UzbekTagger: The rule-based POS tagger for Uzbek language—0
Applying Automated Machine Translation to Educational Video Courses—0
ReVISE: Self-Supervised Speech Resynthesis With Visual Input for Universal and Generalized Speech Regeneration—0
ReVISE: Self-Supervised Speech Resynthesis with Visual Input for Universal and Generalized Speech Enhancement—0
Text-to-speech synthesis based on latent variable conversion using diffusion probabilistic model and variational autoencoder—0
Investigation of Japanese PnG BERT language model in text-to-speech synthesis for pitch accent language—0
Grad-StyleSpeech: Any-speaker Adaptive Text-to-Speech Synthesis with Diffusion Models—0
Technology Pipeline for Large Scale Cross-Lingual Dubbing of Lecture Videos into Multiple Indian Languages—0
Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-To-Speech—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