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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 201–250 of 332 papers

TitleStatusHype
Voice Conversion by Cascading Automatic Speech Recognition and Text-to-Speech Synthesis with Prosody Transfer—0
Multi-speaker Multi-style Text-to-speech Synthesis With Single-speaker Single-style Training Data Scenarios—0
Multi-speaker Text-to-speech Synthesis Using Deep Gaussian Processes—0
Multi-Stage Deep Transfer Learning for EmIoT-enabled Human-Computer Interaction—0
Multi-step Natural Language Understanding—0
Grad-StyleSpeech: Any-speaker Adaptive Text-to-Speech Synthesis with Diffusion Models—0
An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era—0
Neural Harmonic-plus-Noise Waveform Model with Trainable Maximum Voice Frequency for Text-to-Speech Synthesis—0
Neural Models of Text Normalization for Speech Applications—0
Neural Speech Synthesis in German—0
A Novel Data Augmentation Approach for Automatic Speaking Assessment on Opinion Expressions—0
Neural Text Normalization with Subword Units—0
Neural Text-to-Speech Synthesis for an Under-Resourced Language in a Diglossic Environment: the Case of Gascon Occitan—0
Noise-robust zero-shot text-to-speech synthesis conditioned on self-supervised speech-representation model with adapters—0
Normalization of Lithuanian Text Using Regular Expressions—0
Normalization of Non-Standard Words in Croatian Texts—0
Normalizing Text using Language Modelling based on Phonetics and String Similarity—0
Open-Source Boundary-Annotated Corpus for Arabic Speech and Language Processing—0
VQTTS: High-Fidelity Text-to-Speech Synthesis with Self-Supervised VQ Acoustic Feature—0
An objective evaluation of the effects of recording conditions and speaker characteristics in multi-speaker deep neural speech synthesis—0
An In-depth Analysis of the Effect of Text Normalization in Social Media—0
Parallel WaveNet conditioned on VAE latent vectors—0
ParrotTTS: Text-to-Speech synthesis by exploiting self-supervised representations—0
Phonetic Enhanced Language Modeling for Text-to-Speech Synthesis—0
PnG BERT: Augmented BERT on Phonemes and Graphemes for Neural TTS—0
An Experimental Study: Assessing the Combined Framework of WavLM and BEST-RQ for Text-to-Speech Synthesis—0
Predicting Expressive Speaking Style From Text In End-To-End Speech Synthesis—0
Predicting Romanian Stress Assignment—0
Probing Speaker-specific Features in Speaker Representations—0
A Multi-Agent Framework for Automated Qinqiang Opera Script Generation Using Large Language Models—0
PROEMO: Prompt-Driven Text-to-Speech Synthesis Based on Emotion and Intensity Control—0
PSCodec: A Series of High-Fidelity Low-bitrate Neural Speech Codecs Leveraging Prompt Encoders—0
ProsodyFM: Unsupervised Phrasing and Intonation Control for Intelligible Speech Synthesis—0
Prosody-TTS: An end-to-end speech synthesis system with prosody control—0
Pseudo-Autoregressive Neural Codec Language Models for Efficient Zero-Shot Text-to-Speech Synthesis—0
Punjabi Text-To-Speech Synthesis System—0
Wasserstein GAN and Waveform Loss-based Acoustic Model Training for Multi-speaker Text-to-Speech Synthesis Systems Using a WaveNet Vocoder—0
Waveform generation for text-to-speech synthesis using pitch-synchronous multi-scale generative adversarial networks—0
RALL-E: Robust Codec Language Modeling with Chain-of-Thought Prompting for Text-to-Speech Synthesis—0
Real-time Incremental Speech-to-Speech Translation of Dialogs—0
ReCAB-VAE: Gumbel-Softmax Variational Inference Based on Analytic Divergence—0
Refer-iTTS: A System for Referring in Spoken Installments to Objects in Real-World Images—0
Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability—0
DLPO: Diffusion Model Loss-Guided Reinforcement Learning for Fine-Tuning Text-to-Speech Diffusion Models—0
ReVISE: Self-Supervised Speech Resynthesis with Visual Input for Universal and Generalized Speech Enhancement—0
ReVISE: Self-Supervised Speech Resynthesis With Visual Input for Universal and Generalized Speech Regeneration—0
Revival with Voice: Multi-modal Controllable Text-to-Speech Synthesis—0
R-MelNet: Reduced Mel-Spectral Modeling for Neural TTS—0
Robust Zero-Shot Text-to-Speech Synthesis with Reverse Inference Optimization—0
RSS-TOBI - A Prosodically Enhanced Romanian Speech Corpus—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