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

Showing 576–600 of 1249 papers

TitleStatusHype
Ctrl-P: Temporal Control of Prosodic Variation for Speech Synthesis—0
Improving Prosody for Cross-Speaker Style Transfer by Semi-Supervised Style Extractor and Hierarchical Modeling in Speech Synthesis—0
Cross-Utterance Conditioned VAE for Speech Generation—0
Augmenting Images for ASR and TTS through Single-loop and Dual-loop Multimodal Chain Framework—0
Accent conversion using discrete units with parallel data synthesized from controllable accented TTS—0
Improving LPCNet-based Text-to-Speech with Linear Prediction-structured Mixture Density Network—0
Improving homograph disambiguation with supervised machine learning—0
CrossSpeech: Speaker-independent Acoustic Representation for Cross-lingual Speech Synthesis—0
CrossSpeech++: Cross-lingual Speech Synthesis with Decoupled Language and Speaker Generation—0
Improving Prosody for Unseen Texts in Speech Synthesis by Utilizing Linguistic Information and Noisy Data—0
Improving Prosody Modelling with Cross-Utterance BERT Embeddings for End-to-end Speech Synthesis—0
Improving Robustness of Diffusion-Based Zero-Shot Speech Synthesis via Stable Formant Generation—0
Improving Robustness of LLM-based Speech Synthesis by Learning Monotonic Alignment—0
DDOS: A MOS Prediction Framework utilizing Domain Adaptive Pre-training and Distribution of Opinion Scores—0
Improving speech synthesis quality by reducing pitch peaks in the source recordings—0
Improving Trajectory Modelling for DNN-based Speech Synthesis by using Stacked Bottleneck Features and Minimum Generation Error Training—0
Incorporating speaker embedding and post-filter network for improving speaker similarity of personalized speech synthesis system—0
Incremental Coordination: Attention-Centric Speech Production in a Physically Situated Conversational Agent—0
Incremental Disentanglement for Environment-Aware Zero-Shot Text-to-Speech Synthesis—0
Incremental FastPitch: Chunk-based High Quality Text to Speech—0
Incremental Machine Speech Chain Towards Enabling Listening while Speaking in Real-time—0
From Start to Finish: Latency Reduction Strategies for Incremental Speech Synthesis in Simultaneous Speech-to-Speech Translation—0
Incremental Text-to-Speech Synthesis with Prefix-to-Prefix Framework—0
Deep Encoder-Decoder Models for Unsupervised Learning of Controllable Speech Synthesis—0
Improving Cross-lingual Speech Synthesis with Triplet Training Scheme—0
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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