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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
A distributed cloud-based dialog system for conversational application development—0
Code-Mixed Text to Speech Synthesis under Low-Resource Constraints—0
Fast Bootstrapping of Grapheme to Phoneme System for Under-resourced Languages - Application to the Iban Language—0
Chain-of-Thought Training for Open E2E Spoken Dialogue Systems—0
Exploring Transfer Learning for Urdu Speech Synthesis—0
CASSANDRA: A multipurpose configurable voice-enabled human-computer-interface—0
A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI—0
An objective evaluation of the effects of recording conditions and speaker characteristics in multi-speaker deep neural speech synthesis—0
Evaluating Text-to-Speech Synthesis from a Large Discrete Token-based Speech Language Model—0
CapSpeech: Enabling Downstream Applications in Style-Captioned Text-to-Speech—0
Evaluating Long-form Text-to-Speech: Comparing the Ratings of Sentences and Paragraphs—0
BU-TTS: An Open-Source, Bilingual Welsh-English, Text-to-Speech Corpus—0
AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms—0
EPIC TTS Models: Empirical Pruning Investigations Characterizing Text-To-Speech Models—0
Environment Aware Text-to-Speech Synthesis—0
Building Text-to-Speech Systems for Resource Poor Languages—0
Enhancing Zero-shot Text-to-Speech Synthesis with Human Feedback—0
Building a synchronous corpus of acoustic and 3D facial marker data for adaptive audio-visual speech synthesis—0
An In-depth Analysis of the Effect of Text Normalization in Social Media—0
Adaptive Parser-Centric Text Normalization—0
Accented Text-to-Speech Synthesis with Limited Data—0
End-to-End Text-to-Speech using Latent Duration based on VQ-VAE—0
BUCEADOR, a multi-language search engine for digital libraries—0
End-to-End Feedback Loss in Speech Chain Framework via Straight-Through Estimator—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