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AutoTTS: End-to-End Text-to-Speech Synthesis through Differentiable Duration Modeling

arXiv 2203.11049 ↗First posted 21 Mar 2022cs.SD cs.LG eess.AScited by 0

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Abstract (arXiv)

Parallel text-to-speech (TTS) models have recently enabled fast and highly-natural speech synthesis. However, they typically require external alignment models, which are not necessarily optimized for the decoder as they are not jointly trained. In this paper, we propose a differentiable duration method for learning monotonic alignments between input and output sequences. Our method is based on a soft-duration mechanism that optimizes a stochastic process in expectation. Using this differentiable duration method, we introduce AutoTTS, a direct text-to-waveform speech synthesis model. AutoTTS enables high-fidelity speech synthesis through a combination of adversarial training and matching the total ground-truth duration. Experimental results show that our model obtains competitive results while enjoying a much simpler training pipeline. Audio samples are available online.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 33 references that are in the map. First posted on arXiv 21 Mar 2022.

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23 of 33 references.

References · 23

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  3. 2020HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesiscited by 1,139
  4. 2018Self-Attention with Relative Position Representationscited by 1,112
  5. 2016Least Squares Generative Adversarial Networkscited by 1,000
  6. 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 800
  7. 2020FastSpeech 2: Fast and High-Quality End-to-End Text to Speechcited by 710
  8. 2021Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speechcited by 555
  9. 2017Tacotron: Towards End-to-End Speech Synthesiscited by 487
  10. 2021Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speechcited by 445
  11. 2015Autoencoding beyond pixels using a learned similarity metriccited by 407
  12. 2019Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogramcited by 337
11 more
  1. 2020Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Searchcited by 269
  2. 2017Deep Voice: Real-time Neural Text-to-Speechcited by 83
  3. 2020End-to-End Adversarial Text-to-Speechcited by 77
  4. 2017Deep Voice 2: Multi-Speaker Neural Text-to-Speechcited by 75
  5. 2020Parallel Tacotron: Non-Autoregressive and Controllable TTScited by 49
  6. 2019Robust Sequence-to-Sequence Acoustic Modeling with Stepwise Monotonic Attention for Neural TTScited by 33
  7. 2021Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modelingcited by 31
  8. 2020AlignTTS: Efficient Feed-Forward Text-to-Speech System without Explicit Alignmentcited by 20
  9. 2020SpeedySpeech: Efficient Neural Speech Synthesiscited by 15
  10. 2020EfficientTTS: An Efficient and High-Quality Text-to-Speech Architecturecited by 14
  11. 2020JDI-T: Jointly trained Duration Informed Transformer for Text-To-Speech without Explicit Alignmentcited by 13

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