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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 151–200 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
An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era—0
Controllable Accented Text-to-Speech Synthesis—0
EPIC TTS Models: Empirical Pruning Investigations Characterizing Text-To-Speech Models—0
Mlphon: A Multifunctional Grapheme-Phoneme Conversion Tool Using Finite State TransducersCode0
BERT, can HE predict contrastive focus? Predicting and controlling prominence in neural TTS using a language model—0
R-MelNet: Reduced Mel-Spectral Modeling for Neural TTS—0
Exploring Transfer Learning for Urdu Speech Synthesis—0
BU-TTS: An Open-Source, Bilingual Welsh-English, Text-to-Speech Corpus—0
Investigating Inter- and Intra-speaker Voice Conversion using Audiobooks—0
Preparing an Endangered Language for the Digital Age: The Case of Judeo-SpanishCode0
ReCAB-VAE: Gumbel-Softmax Variational Inference Based on Analytic Divergence—0
Systematic Inequalities in Language Technology Performance across the World’s LanguagesCode0
The PartialSpoof Database and Countermeasures for the Detection of Short Fake Speech Segments Embedded in an Utterance—0
SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis—0
VQTTS: High-Fidelity Text-to-Speech Synthesis with Self-Supervised VQ Acoustic Feature—0
Applying Syntaxx2013Prosody Mapping Hypothesis and Prosodic Well-Formedness Constraints to Neural Sequence-to-Sequence Speech Synthesis—0
AutoTTS: End-to-End Text-to-Speech Synthesis through Differentiable Duration Modeling—0
ECAPA-TDNN for Multi-speaker Text-to-speech SynthesisCode0
Text-free non-parallel many-to-many voice conversion using normalising flows—0
Deep Performer: Score-to-Audio Music Performance Synthesis—0
Multi-Stage Deep Transfer Learning for EmIoT-enabled Human-Computer Interaction—0
Transformer-based Models of Text Normalization for Speech Applications—0
Multi-speaker Multi-style Text-to-speech Synthesis With Single-speaker Single-style Training Data Scenarios—0
Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance—0
Systematic Inequalities in Language Technology Performance across the World's LanguagesCode0
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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