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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 226–250 of 332 papers

TitleStatusHype
Multi-speaker Text-to-speech Synthesis Using Deep Gaussian Processes—0
Normalizing Text using Language Modelling based on Phonetics and String Similarity—0
FastSpeech 2: Fast and High-Quality End-to-End Text to SpeechCode1
End-to-End Adversarial Text-to-SpeechCode1
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment SearchCode1
Investigation of learning abilities on linguistic features in sequence-to-sequence text-to-speech synthesis—0
Semi-supervised Learning for Multi-speaker Text-to-speech Synthesis Using Discrete Speech Representation—0
Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech SynthesisCode1
Neural Text-to-Speech Synthesis for an Under-Resourced Language in a Diglossic Environment: the Case of Gascon Occitan—0
Style Variation as a Vantage Point for Code-Switching—0
Comparison of Speech Representations for Automatic Quality Estimation in Multi-Speaker Text-to-Speech SynthesisCode0
Using VAEs and Normalizing Flows for One-shot Text-To-Speech Synthesis of Expressive Speech—0
Cross-lingual Multi-speaker Text-to-speech Synthesis for Voice Cloning without Using Parallel Corpus for Unseen Speakers—0
Independent and automatic evaluation of acoustic-to-articulatory inversion modelsCode0
A unified sequence-to-sequence front-end model for Mandarin text-to-speech synthesis—0
Incremental Text-to-Speech Synthesis with Prefix-to-Prefix Framework—0
Spoofing Speaker Verification Systems with Deep Multi-speaker Text-to-speech SynthesisCode0
Effect of choice of probability distribution, randomness, and search methods for alignment modeling in sequence-to-sequence text-to-speech synthesis using hard alignment—0
Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogramCode2
The Theory behind Controllable Expressive Speech Synthesis: a Cross-disciplinary Approach—0
Modular Meta-Learning with Shrinkage—0
Evaluating Long-form Text-to-Speech: Comparing the Ratings of Sentences and Paragraphs—0
Neural Harmonic-plus-Noise Waveform Model with Trainable Maximum Voice Frequency for Text-to-Speech Synthesis—0
MelNet: A Generative Model for Audio in the Frequency DomainCode0
Listening while Speaking and Visualizing: Improving ASR through Multimodal Chain—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