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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 101–125 of 332 papers

TitleStatusHype
Mlphon: A Multifunctional Grapheme-Phoneme Conversion Tool Using Finite State TransducersCode0
Independent and automatic evaluation of acoustic-to-articulatory inversion modelsCode0
Investigation of enhanced Tacotron text-to-speech synthesis systems with self-attention for pitch accent languageCode0
Speech Synthesis from Text and Ultrasound Tongue Image-based Articulatory InputCode0
Creating New Language and Voice Components for the Updated MaryTTS Text-to-Speech Synthesis Platform—0
Controllable Prosody Generation With Partial Inputs—0
A unified sequence-to-sequence front-end model for Mandarin text-to-speech synthesis—0
Controllable neural text-to-speech synthesis using intuitive prosodic features—0
Controllable Accented Text-to-Speech Synthesis—0
A unified front-end framework for English text-to-speech synthesis—0
An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era—0
Continual Speaker Adaptation for Text-to-Speech Synthesis—0
Full-text Error Correction for Chinese Speech Recognition with Large Language Model—0
FMSD-TTS: Few-shot Multi-Speaker Multi-Dialect Text-to-Speech Synthesis for Ü-Tsang, Amdo and Kham Speech Dataset Generation—0
Conditioning Sequence-to-sequence Networks with Learned Activations—0
A Unified Framework for Collecting Text-to-Speech Synthesis Datasets for 22 Indian Languages—0
FLY-TTS: Fast, Lightweight and High-Quality End-to-End Text-to-Speech Synthesis—0
Generative adversarial network-based glottal waveform model for statistical parametric speech synthesis—0
Flavored Tacotron: Conditional Learning for Prosodic-linguistic Features—0
Fine-grained Style Modeling, Transfer and Prediction in Text-to-Speech Synthesis via Phone-Level Content-Style Disentanglement—0
Generative Pre-training for Speech with Flow Matching—0
Generative Semantic Communication for Text-to-Speech Synthesis—0
Comparing normalizing flows and diffusion models for prosody and acoustic modelling in text-to-speech—0
Augmenting Images for ASR and TTS through Single-loop and Dual-loop Multimodal Chain Framework—0
A Novel Data Augmentation Approach for Automatic Speaking Assessment on Opinion Expressions—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