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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 101–150 of 332 papers

TitleStatusHype
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
Enhancing Suno's Bark Text-to-Speech Model: Addressing Limitations Through Meta's Encodec and Pre-Trained HubertCode4
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
Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language ModelingCode5
ParrotTTS: Text-to-Speech synthesis by exploiting self-supervised representations—0
Imaginary Voice: Face-styled Diffusion Model for Text-to-SpeechCode1
A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous SpeechCode2
UzbekTagger: The rule-based POS tagger for Uzbek language—0
Applying Automated Machine Translation to Educational Video Courses—0
Neural Codec Language Models are Zero-Shot Text to Speech SynthesizersCode7
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
RWEN-TTS: Relation-aware Word Encoding Network for Natural Text-to-Speech SynthesisCode1
MnTTS2: An Open-Source Multi-Speaker Mongolian Text-to-Speech Synthesis DatasetCode1
Towards Building Text-To-Speech Systems for the Next Billion UsersCode2
Grad-StyleSpeech: Any-speaker Adaptive Text-to-Speech Synthesis with Diffusion Models—0
OverFlow: Putting flows on top of neural transducers for better TTSCode1
ERNIE-SAT: Speech and Text Joint Pretraining for Cross-Lingual Multi-Speaker Text-to-SpeechCode6
Accented Text-to-Speech Synthesis with a Conditional Variational AutoencoderCode1
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
MnTTS: An Open-Source Mongolian Text-to-Speech Synthesis Dataset and Accompanied BaselineCode1
EPIC TTS Models: Empirical Pruning Investigations Characterizing Text-To-Speech Models—0
Mlphon: A Multifunctional Grapheme-Phoneme Conversion Tool Using Finite State TransducersCode0
ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-SpeechCode3
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
Automatic Prosody Annotation with Pre-Trained Text-Speech ModelCode1
BU-TTS: An Open-Source, Bilingual Welsh-English, Text-to-Speech Corpus—0
Exploring Transfer Learning for Urdu Speech Synthesis—0
Investigating Inter- and Intra-speaker Voice Conversion using Audiobooks—0
Preparing an Endangered Language for the Digital Age: The Case of Judeo-SpanishCode0
StyleTTS: A Style-Based Generative Model for Natural and Diverse Text-to-Speech SynthesisCode2
PaddleSpeech: An Easy-to-Use All-in-One Speech ToolkitCode6
GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-SpeechCode2
ReCAB-VAE: Gumbel-Softmax Variational Inference Based on Analytic Divergence—0
NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level QualityCode2
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