mlsanityData to 14 Oct 2026

Exploring Machine Speech Chain for Domain Adaptation and Few-Shot Speaker Adaptation

arXiv 2104.03815 ↗First posted 8 Apr 2021cs.CL cs.SD eess.AScited by 4

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Abstract (arXiv)

Machine Speech Chain, which integrates both end-to-end (E2E) automatic speech recognition (ASR) and text-to-speech (TTS) into one circle for joint training, has been proven to be effective in data augmentation by leveraging large amounts of unpaired data. In this paper, we explore the TTS->ASR pipeline in speech chain to do domain adaptation for both neural TTS and E2E ASR models, with only text data from target domain. We conduct experiments by adapting from audiobook domain (LibriSpeech) to presentation domain (TED-LIUM), there is a relative word error rate (WER) reduction of 10% for the E2E ASR model on the TED-LIUM test set, and a relative WER reduction of 51.5% in synthetic speech generated by neural TTS in the presentation domain. Further, we apply few-shot speaker adaptation for the E2E ASR by using a few utterances from target speakers in an unsupervised way, results in additional gains.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 14 of its 35 references that are in the map. First posted on arXiv 8 Apr 2021.

Builds on

14 of 35 references.

References · 14

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2018VoxCeleb2: Deep Speaker Recognitioncited by 875
  3. 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 804
  4. 2017VoxCeleb: a large-scale speaker identification datasetcited by 732
  5. 2019LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speechcited by 649
  6. 2018ESPnet: End-to-End Speech Processing Toolkitcited by 558
  7. 2018Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesiscited by 282
  8. 2017Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attentioncited by 57
  9. 2019Generating Synthetic Audio Data for Attention-Based Speech Recognition Systemscited by 47
  10. 2017Listening while Speaking: Speech Chain by Deep Learningcited by 46
  11. 2019Robust Sequence-to-Sequence Acoustic Modeling with Stepwise Monotonic Attention for Neural TTScited by 33
  12. 2018Machine Speech Chain with One-shot Speaker Adaptationcited by 11
2 more
  1. 2018Transfer learning of language-independent end-to-end ASR with language model fusioncited by 10
  2. 2019Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Textcited by 10

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Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 0; 2025: 1.

4 papers.

  1. 2021A Survey on Neural Speech Synthesiscited by 184
  2. 2023Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesiscited by 10
  3. 2023SpeeChain: A Speech Toolkit for Large-Scale Machine Speech Chaincited by 2
  4. 2025TokenChain: A Discrete Speech Chain via Semantic Token Modelingcited by 0