Speech Synthesis

Speech synthesis is the task of generating speech from some other modality like text, lip movements etc.

Please note that the leaderboards here are not really comparable between studies - as they use mean opinion score as a metric and collect different samples from Amazon Mechnical Turk.

( Image credit: WaveNet: A generative model for raw audio )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 571–580 of 1249 papers

Title	Date	Tasks	Status
DNN-based Speaker Embedding Using Subjective Inter-speaker Similarity for Multi-speaker Modeling in Speech Synthesis	Jul 19, 2019	Speech Synthesis	—Unverified
Bayesian Subspace HMM for the Zerospeech 2020 Challenge	May 19, 2020	Speech Synthesis	—Unverified
結合ANN、全域變異數與真實軌跡挑選之基週軌跡產生方法(A Pitch-contour Generation Method Combining ANN Prediction,Global Variance Matching, and Real-contour Selection)[In Chinese]	Oct 1, 2015	Speech Synthesis	—Unverified
DMOSpeech: Direct Metric Optimization via Distilled Diffusion Model in Zero-Shot Speech Synthesis	Oct 14, 2024	DenoisingSpeaker Verification	—Unverified
Balancing Speech Understanding and Generation Using Continual Pre-training for Codec-based Speech LLM	Feb 24, 2025	Automatic Speech RecognitionLanguage Modeling	—Unverified
An Initial study on Birdsong Re-synthesis Using Neural Vocoders	Sep 21, 2022	ResynthesisSpeech Synthesis	—Unverified
Empowering Communication: Speech Technology for Indian and Western Accents through AI-powered Speech Synthesis	Jan 22, 2024	Speaker VerificationSpeech Synthesis	—Unverified
A Challenge Set and Methods for Noun-Verb Ambiguity	Oct 1, 2018	Speech Synthesistext-to-speech	—Unverified
完全基於類神經網路之語音合成系統初步研究 (A Preliminary Study on Fully Neural Network-based Speech Synthesis System) [In Chinese]	Nov 1, 2017	Speech Synthesis	—Unverified
Disentangling Correlated Speaker and Noise for Speech Synthesis via Data Augmentation and Adversarial Factorization	Oct 30, 2018	Data AugmentationDisentanglement	—Unverified

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All datasets LibriTTS North American English LJSpeech Mandarin Chinese Blizzard Challenge 2013

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	PeriodWave-Turbo-L	PESQ	4.45	—	Unverified
2	BigVGAN-v2	PESQ	4.36	—	Unverified
3	EVA-GAN-big	PESQ	4.35	—	Unverified
4	PeriodWave + FreeU	PESQ	4.25	—	Unverified
5	RFWave	PESQ	4.23	—	Unverified
6	BigVSAN (w/ snakebeta)	PESQ	4.12	—	Unverified
7	BigVSAN	PESQ	4.12	—	Unverified
8	EVA-GAN-base	PESQ	4.03	—	Unverified
9	BigVGAN	PESQ	4.03	—	Unverified
10	Vocos	PESQ	3.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Tacotron 2	Mean Opinion Score	4.53	—	Unverified
2	WaveNet (Linguistic)	Mean Opinion Score	4.34	—	Unverified
3	WaveNet (L+F)	Mean Opinion Score	4.21	—	Unverified
4	Tacotron	Mean Opinion Score	4	—	Unverified
5	HMM-driven concatenative	Mean Opinion Score	3.86	—	Unverified
6	LSTM-RNN parametric	Mean Opinion Score	3.67	—	Unverified
7	means	Mean Opinion Score	0	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BDDM vocoder	Mean Opinion Score	4.48	—	Unverified
2	DiffWave LARGE	Mean Opinion Score	4.44	—	Unverified
3	Neural HMM	Mean Opinion Score	3.24	—	Unverified
4	Neural HMM Ablation with 1 state per phone	Mean Opinion Score	2.68	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	WaveNet (L+F)	Mean Opinion Score	4.08	—	Unverified
2	LSTM-RNN parametric	Mean Opinion Score	3.79	—	Unverified
3	HMM-driven concatenative	Mean Opinion Score	3.47	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SampleRNN (2-tier)	NLL	1.39	—	Unverified
2	SampleRNN (3-tier)	NLL	1.39	—	Unverified