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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 176–200 of 332 papers

TitleStatusHype
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