Prosody-TTS: Improving Prosody with Masked Autoencoder and Conditional Diffusion Model For Expressive Text-to-Speech (2023.findings-acl)
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| Challenge: | Expressive text-to-speech aims to generate high-quality samples with rich prosody . prosodic attributes in highly dynamic voices are difficult to capture and model without intonation . |
| Approach: | They propose a pipeline that enhances prosody modeling and sampling by introducing a self-supervised masked autoencoder and a diffusion model to sample diverse prosodic patterns within the latent space. |
| Outcome: | The proposed pipeline achieves new state-of-the-art in text-to-speech with natural and expressive synthesis. |
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| Challenge: | Text-to-speech (TTS) models have been developed to generate high-quality speech. |
| Approach: | They propose an end-to-end TTS model that integrates large self-supervised speech models and conditional flow matching to model prosodic features effectively. |
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DPP-TTS: Diversifying prosodic features of speech via determinantal point processes (2023.emnlp-main)
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| Challenge: | Recent advances in deep generative models have succeeded in synthesizing human-like speech. |
| Approach: | They propose a text-to-speech model with a prosody diversifying module that considers perceptual diversity in each sample and among multiple samples. |
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DiffStyleTTS: Diffusion-based Hierarchical Prosody Modeling for Text-to-Speech with Diverse and Controllable Styles (2025.coling-main)
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| Challenge: | Existing models for text-to-speech (TTS) synthesize speech with acoustic features . autoregressive models have problems with word skipping and repeated reading . non-autoregressive acustic models lack probabilistic modeling and unimodal characteristics of Gaussian distribution don't conform to true distribution of aural features, which restricts the diversity of generated prosodic features. |
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| Challenge: | Recent advances in generative language modeling applied to discrete speech tokens presented a new avenue for text-to-speech (TTS) synthesis. |
| Approach: | They propose to use generative language modeling to generate text-to-speech (TTS) outputs by a discrete token-based model. |
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CLAPSpeech: Learning Prosody from Text Context with Contrastive Language-Audio Pre-Training (2023.acl-long)
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| Challenge: | Existing methods for expressive text-to-speech only implicitly learn prosody with masked token reconstruction tasks. |
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Computational Narrative Understanding for Expressive Text-to-Speech (2026.findings-acl)
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| Challenge: | Recent advances in text-to-speech systems have been driven by large, multi-domain speech corpora. |
| Approach: | They propose a large-scale 5.3K hours of expressive speech drawn from character quotations . they fine-tune a flow-matching model and train from scratch . |
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Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech (2022.acl-long)
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| Challenge: | Experimental results show that the proposed model improves naturalness and prosody diversity with clear margins. |
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| Challenge: | Text-to-Speech (TTS) training requires extensive and diverse text and speech data. |
| Approach: | They propose a synthetic speech data generation pipeline that generates multilingual, domain-specific datasets for TTS training. |
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ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer (2023.emnlp-main)
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| Challenge: | Text-to-speech (TTS) performance has improved with the advent of denoising Diffusion Probabilistic Models . however, perceived quality of audio depends on content, pitch, rhythm, and energy . |
| Approach: | They propose a visual TTS model with scalable diffusion transformers that complement phoneme sequences with visual information to generate high-perceived audio. |
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On the Semantic Latent Space of Diffusion-Based Text-To-Speech Models (2024.acl-short)
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| Challenge: | Denoising Diffusion Models (DDMs) are a powerful generative tool for text-to-speech (TTS) but their semantic capabilities are unknown and control of synthesized speech’s vocal properties remains a challenge. |
| Approach: | They explore the latent space of frozen TTS models composed of latent bottleneck activations of the DDM’s denoiser and propose methods for finding semantic directions within it. |
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