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Diff-TTS: A Denoising Diffusion Model for Text-to-Speech

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arxiv 2104.01409 v1 pith:YLYFYEOX submitted 2021-04-03 eess.AS cs.AIcs.SD

Myeonghun Jeong , Hyeongju Kim , Sung Jun Cheon , Byoung Jin Choi , Nam Soo Kim This is my paper · ORCID
classification eess.AScs.AIcs.SD
keywords diff-ttsdiffusiondenoisingfastermel-spectrogrammethodmodelspeech
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Although neural text-to-speech (TTS) models have attracted a lot of attention and succeeded in generating human-like speech, there is still room for improvements to its naturalness and architectural efficiency. In this work, we propose a novel non-autoregressive TTS model, namely Diff-TTS, which achieves highly natural and efficient speech synthesis. Given the text, Diff-TTS exploits a denoising diffusion framework to transform the noise signal into a mel-spectrogram via diffusion time steps. In order to learn the mel-spectrogram distribution conditioned on the text, we present a likelihood-based optimization method for TTS. Furthermore, to boost up the inference speed, we leverage the accelerated sampling method that allows Diff-TTS to generate raw waveforms much faster without significantly degrading perceptual quality. Through experiments, we verified that Diff-TTS generates 28 times faster than the real-time with a single NVIDIA 2080Ti GPU.

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Forward citations

Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inference

    cs.LG 2026-07 conditional novelty 6.0 of 10

    A two-layer Tsetlin Machine ensemble with gossip-based vote sharing matches centralized accuracy on several benchmarks without exchanging raw data.

  2. DiTReducio: A Training-Free Acceleration for DiT-Based TTS via Progressive Calibration

    cs.SD 2025-09 conditional novelty 6.0 of 10

    DiTReducio is a training-free, pattern-guided layer and branch skipping method that accelerates DiT-based TTS, reporting significant FLOP and RTF reductions with modest quality loss at tuned thresholds.

  3. Continuous Autoregressive Modeling with Stochastic Monotonic Alignment for Speech Synthesis

    cs.LG 2025-02 conditional novelty 6.0 of 10

    Continuous autoregressive text-to-speech with a Gaussian-mixture codec matches or beats a discrete-codec VALL-E baseline with a fraction of the language model parameters.

  4. BiDM: Pushing the Limit of Quantization for Diffusion Models

    cs.CV 2024-12 conditional novelty 6.0 of 10

    BiDM achieves the first fully binarized (W1A1) diffusion model, with FID 22.74 on LSUN-Bedrooms, by adding timestep-aware binary structures and patch-based distillation.

  5. CloneShield: A Framework for Universal Perturbation Against Zero-Shot Voice Cloning

    cs.SD 2025-05 reject novelty 5.0 of 10

    A universal adversarial perturbation framework claiming to protect speech against zero-shot voice cloning by degrading cloned outputs while preserving input naturalness.

  6. Towards Flow-Matching-based TTS without Classifier-Free Guidance

    eess.AS 2025-04 reject novelty 5.0 of 10

    Modifying the flow-matching training target lets F5-TTS synthesize speech without classifier-free guidance at inference, halving per-step cost and improving measured WER, SIM-O, and MOS.

  7. UIBDiffusion: Universal Imperceptible Backdoor Attack for Diffusion Models

    cs.CR 2024-12 reject novelty 5.0 of 10

    UIBDiffusion uses imperceptible universal adversarial perturbations as backdoor triggers for diffusion models, claiming high attack success and evasion of Elijah and TERD.

  8. I2TTS: Image-indicated Immersive Text-to-speech Synthesis with Spatial Perception

    cs.SD 2024-11 reject novelty 5.0 of 10

    I2TTS is an end-to-end TTS that conditions on CLIP image features and a frozen reverberation classifier to synthesize scene-matched, speaker-adaptive speech, reporting gains on SRE, MOS, and WER.

  9. Constraint-Aware Diffusion Guidance for Robotics: Real-Time Obstacle Avoidance for Autonomous Racing

    cs.RO 2025-05 conditional novelty 4.0 of 10

    A diffusion trajectory planner with a barrier-function guidance term and warm starting avoids obstacles in real time on a miniature race car, with 100% success in the reported trials.

  10. Language translation, and change of accent for speech-to-speech task using diffusion model

    cs.CL 2025-05 reject novelty 3.0 of 10

    A diffusion-based pipeline is proposed for simultaneous language translation and accent change, but only text-to-speech subtasks are evaluated and the combined S2ST result is not demonstrated.

  11. Survey on AI-Generated Media Detection: From Non-MLLM to MLLM

    cs.CV 2025-02 unverdicted novelty 3.0 of 10

    A survey organizing AI-generated media detection into Non-MLLM and MLLM based methods, with task and benchmark taxonomies.

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