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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 76–100 of 332 papers

TitleStatusHype
Tacotron: Towards End-to-End Speech SynthesisCode1
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
Conditioning Sequence-to-sequence Networks with Learned Activations—0
A Unified Framework for Collecting Text-to-Speech Synthesis Datasets for 22 Indian Languages—0
Augmenting Images for ASR and TTS through Single-loop and Dual-loop Multimodal Chain Framework—0
Comparing normalizing flows and diffusion models for prosody and acoustic modelling in text-to-speech—0
A Novel Data Augmentation Approach for Automatic Speaking Assessment on Opinion Expressions—0
A distributed cloud-based dialog system for conversational application development—0
Listening while Speaking and Visualizing: Improving ASR through Multimodal Chain—0
Code-Mixed Text to Speech Synthesis under Low-Resource Constraints—0
Chain-of-Thought Training for Open E2E Spoken Dialogue Systems—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
CapSpeech: Enabling Downstream Applications in Style-Captioned Text-to-Speech—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
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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