mlsanityData to 7 Oct 2026

Adapitch: Adaption Multi-Speaker Text-to-Speech Conditioned on Pitch Disentangling with Untranscribed Data

arXiv 2210.13803 ↗First posted 25 Oct 2022cs.SD cs.CL eess.AScited by 0

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Abstract (arXiv)

In this paper, we proposed Adapitch, a multi-speaker TTS method that makes adaptation of the supervised module with untranscribed data. We design two self supervised modules to train the text encoder and mel decoder separately with untranscribed data to enhance the representation of text and mel. To better handle the prosody information in a synthesized voice, a supervised TTS module is designed conditioned on content disentangling of pitch, text, and speaker. The training phase was separated into two parts, pretrained and fixed the text encoder and mel decoder with unsupervised mode, then the supervised mode on the disentanglement of TTS. Experiment results show that the Adaptich achieved much better quality than baseline methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 20 references that are in the map. First posted on arXiv 25 Oct 2022.

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References · 12

  1. 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 800
  2. 2020FastSpeech 2: Fast and High-Quality End-to-End Text to Speechcited by 710
  3. 2019LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speechcited by 639
  4. 2021A Survey on Neural Speech Synthesiscited by 182
  5. 2020FastPitch: Parallel Text-to-speech with Pitch Predictioncited by 150
  6. 2020Low-resource expressive text-to-speech using data augmentationcited by 26
  7. 2022AVQVC: One-shot Voice Conversion by Vector Quantization with applying contrastive learningcited by 24
  8. 2019Towards Unsupervised Speech Recognition and Synthesis with Quantized Speech Representation Learningcited by 16
  9. 2022nnSpeech: Speaker-Guided Conditional Variational Autoencoder for Zero-shot Multi-speaker Text-to-Speechcited by 10
  10. 2022TDASS: Target Domain Adaptation Speech Synthesis Framework for Multi-speaker Low-Resource TTScited by 5
  11. 2019A Comparative Study of Pitch Extraction Algorithms on a Large Variety of Singing Soundscited by 3
  12. 2022SUSing: SU-net for Singing Voice Synthesiscited by 3

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