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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 126–150 of 332 papers

TitleStatusHype
An Investigation of the Relation Between Grapheme Embeddings and Pronunciation for Tacotron-based Systems—0
Emphasized Accent Phrase Prediction from Text for Advertisement Text-To-Speech Synthesis—0
BERT, can HE predict contrastive focus? Predicting and controlling prominence in neural TTS using a language model—0
Efficient training strategies for natural sounding speech synthesis and speaker adaptation based on FastPitch—0
ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech—0
An Experimental Study: Assessing the Combined Framework of WavLM and BEST-RQ for Text-to-Speech Synthesis—0
Accent conversion using discrete units with parallel data synthesized from controllable accented TTS—0
Efficient Generative Modeling with Residual Vector Quantization-Based Tokens—0
Bahasa Harmony: A Comprehensive Dataset for Bahasa Text-to-Speech Synthesis with Discrete Codec Modeling of EnGen-TTS—0
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
BAD: An Assistant tool for making verses in Basque—0
AS-Speech: Adaptive Style For Speech Synthesis—0
DurIAN-E: Duration Informed Attention Network For Expressive Text-to-Speech Synthesis—0
A Multi-Agent Framework for Automated Qinqiang Opera Script Generation Using Large Language Models—0
A Challenge Set and Methods for Noun-Verb Ambiguity—0
Large tagset labeling using Feed Forward Neural Networks. Case study on Romanian Language—0
DurIAN-E 2: Duration Informed Attention Network with Adaptive Variational Autoencoder and Adversarial Learning for Expressive Text-to-Speech Synthesis—0
Duration Modeling by Multi-Models based on Vowel Production characteristics—0
AutoStyle-TTS: Retrieval-Augmented Generation based Automatic Style Matching Text-to-Speech Synthesis—0
Dual Script E2E framework for Multilingual and Code-Switching ASR—0
Do Prosody Transfer Models Transfer Prosody?—0
Autoregressive Speech Synthesis without Vector Quantization—0
A Review of Deep Learning Techniques for Speech Processing—0
DNN-based Speech Synthesis for Indian Languages from ASCII text—0
Applying Syntaxx2013Prosody Mapping Hypothesis and Prosodic Well-Formedness Constraints to Neural Sequence-to-Sequence Speech Synthesis—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