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 1181–1190 of 1249 papers

Title	Date	Tasks	Status
DurIAN-E: Duration Informed Attention Network For Expressive Text-to-Speech Synthesis	Sep 22, 2023	DenoisingSpeech Synthesis	—Unverified
Dynamic Prosody Generation for Speech Synthesis using Linguistics-Driven Acoustic Embedding Selection	Dec 2, 2019	Speech Synthesistext-to-speech	—Unverified
ED-TTS: Multi-Scale Emotion Modeling using Cross-Domain Emotion Diarization for Emotional Speech Synthesis	Jan 16, 2024	DenoisingEmotional Speech Synthesis	—Unverified
Effect of choice of probability distribution, randomness, and search methods for alignment modeling in sequence-to-sequence text-to-speech synthesis using hard alignment	Oct 28, 2019	Hard AttentionSpeech Synthesis	—Unverified
Effect of data reduction on sequence-to-sequence neural TTS	Nov 15, 2018	Speech Synthesis	—Unverified
Efficient Generative Modeling with Residual Vector Quantization-Based Tokens	Dec 13, 2024	Conditional Image GenerationImage Generation	—Unverified
Efficient Incremental Text-to-Speech on GPUs	Nov 25, 2022	GPUSpeech Synthesis	—Unverified
Efficient neural speech synthesis for low-resource languages through multilingual modeling	Aug 20, 2020	Speech Synthesis	—Unverified
Efficient training strategies for natural sounding speech synthesis and speaker adaptation based on FastPitch	Oct 9, 2024	Speech Synthesistext-to-speech	—Unverified
ET-GAN: Cross-Language Emotion Transfer Based on Cycle-Consistent Generative Adversarial Networks	May 27, 2019	Domain AdaptationGenerative Adversarial Network	—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