SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 726–750 of 1249 papers

TitleStatusHype
Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance—0
Word-Level Style Control for Expressive, Non-attentive Speech Synthesis—0
Prosodic Clustering for Phoneme-level Prosody Control in End-to-End Speech Synthesis—0
High Quality Streaming Speech Synthesis with Low, Sentence-Length-Independent Latency—0
Cross-lingual Low Resource Speaker Adaptation Using Phonological Features—0
Speech Synthesis for Low Resource Languages using Transliteration Enabled Transfer Learning—0
Modeling speech recognition and synthesis simultaneously: Encoding and decoding lexical and sublexical semantic information into speech with no access to speech data—0
Improving Prosody for Unseen Texts in Speech Synthesis by Utilizing Linguistic Information and Noisy Data—0
Cross-lingual Transfer for Speech Processing using Acoustic Language SimilarityCode0
fairseq Sˆ2: A Scalable and Integrable Speech Synthesis Toolkit—0
Assessing Evaluation Metrics for Speech-to-Speech Translation—0
DelightfulTTS: The Microsoft Speech Synthesis System for Blizzard Challenge 2021Code0
Synt++: Utilizing Imperfect Synthetic Data to Improve Speech Recognition—0
CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing FlowsCode0
From Start to Finish: Latency Reduction Strategies for Incremental Speech Synthesis in Simultaneous Speech-to-Speech Translation—0
Direct Simultaneous Speech-to-Speech Translation with Variational Monotonic Multihead Attention—0
SingGAN: Generative Adversarial Network For High-Fidelity Singing Voice Generation—0
DeepA: A Deep Neural Analyzer For Speech And Singing Vocoding—0
Systematic Inequalities in Language Technology Performance across the World's LanguagesCode0
LaughNet: synthesizing laughter utterances from waveform silhouettes and a single laughter example—0
Using multiple reference audios and style embedding constraints for speech synthesis—0
Towards Lifelong Learning of Multilingual Text-To-Speech SynthesisCode0
Environment Aware Text-to-Speech Synthesis—0
VisualTTS: TTS with Accurate Lip-Speech Synchronization for Automatic Voice Over—0
Cloning one's voice using very limited data in the wild—0
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All datasetsLibriTTSNorth American EnglishLJSpeechMandarin ChineseBlizzard Challenge 2013

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PeriodWave-Turbo-LPESQ4.45—Unverified
2BigVGAN-v2PESQ4.36—Unverified
3EVA-GAN-bigPESQ4.35—Unverified
4PeriodWave + FreeUPESQ4.25—Unverified
5RFWavePESQ4.23—Unverified
6BigVSAN (w/ snakebeta)PESQ4.12—Unverified
7BigVSANPESQ4.12—Unverified
8EVA-GAN-basePESQ4.03—Unverified
9BigVGANPESQ4.03—Unverified
10VocosPESQ3.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Tacotron 2Mean Opinion Score4.53—Unverified
2WaveNet (Linguistic)Mean Opinion Score4.34—Unverified
3WaveNet (L+F)Mean Opinion Score4.21—Unverified
4TacotronMean Opinion Score4—Unverified
5HMM-driven concatenativeMean Opinion Score3.86—Unverified
6LSTM-RNN parametricMean Opinion Score3.67—Unverified
7meansMean Opinion Score0—Unverified
#ModelMetricClaimedVerifiedStatus
1BDDM vocoderMean Opinion Score4.48—Unverified
2DiffWave LARGEMean Opinion Score4.44—Unverified
3Neural HMMMean Opinion Score3.24—Unverified
4Neural HMM Ablation with 1 state per phoneMean Opinion Score2.68—Unverified
#ModelMetricClaimedVerifiedStatus
1WaveNet (L+F)Mean Opinion Score4.08—Unverified
2LSTM-RNN parametricMean Opinion Score3.79—Unverified
3HMM-driven concatenativeMean Opinion Score3.47—Unverified
#ModelMetricClaimedVerifiedStatus
1SampleRNN (2-tier)NLL1.39—Unverified
2SampleRNN (3-tier)NLL1.39—Unverified