mlsanityData to 7 Oct 2026

You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation

arXiv 2005.07157 ↗First posted 14 May 2020eess.AS cs.CL cs.LGcited by 29

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Abstract (arXiv)

Data augmentation is one of the most effective ways to make end-to-end automatic speech recognition (ASR) perform close to the conventional hybrid approach, especially when dealing with low-resource tasks. Using recent advances in speech synthesis (text-to-speech, or TTS), we build our TTS system on an ASR training database and then extend the data with synthesized speech to train a recognition model. We argue that, when the training data amount is relatively low, this approach can allow an end-to-end model to reach hybrid systems' quality. For an artificial low-to-medium-resource setup, we compare the proposed augmentation with the semi-supervised learning technique. We also investigate the influence of vocoder usage on final ASR performance by comparing Griffin-Lim algorithm with our modified LPCNet. When applied with an external language model, our approach outperforms a semi-supervised setup for LibriSpeech test-clean and only 33% worse than a comparable supervised setup. Our system establishes a competitive result for end-to-end ASR trained on LibriSpeech train-clean-100 set with WER 4.3% for test-clean and 13.5% for test-other.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 May 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 6; 2023: 7; 2024: 7; 2025: 0; 2026: 2.

29 papers.

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  1. 2022Self-Supervised Speech Representation Learning: A Reviewcited by 258
  2. 2021Voice Conversion Can Improve ASR in Very Low-Resource Settingscited by 13
  3. 2022End-to-end model for named entity recognition from speech without paired training datacited by 11
  4. 2023Text-only domain adaptation for end-to-end ASR using integrated text-to-mel-spectrogram generatorcited by 11
  5. 2024Enhancing Low-Resource ASR through Versatile TTS: Bridging the Data Gapcited by 11
  6. 2022ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversioncited by 10
  7. 2021On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASRcited by 9
  8. 2021Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architecturescited by 8
  9. 2022Learning Speech Emotion Representations in the Quaternion Domaincited by 7
  10. 2022A Simple Baseline for Domain Adaptation in End to End ASR Systems Using Synthetic Datacited by 7
  11. 2023Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errorscited by 6
  12. 2023OLISIA: a Cascade System for Spoken Dialogue State Trackingcited by 5
17 more
  1. 2023On the Relevance of Phoneme Duration Variability of Synthesized Training Data for Automatic Speech Recognitioncited by 4
  2. 2024On the Effect of Purely Synthetic Training Data for Different Automatic Speech Recognition Architecturescited by 4
  3. 2024On the Problem of Text-To-Speech Model Selection for Synthetic Data Generation in Automatic Speech Recognitioncited by 3
  4. 2021Dynamic Acoustic Unit Augmentation With BPE-Dropout for Low-Resource End-to-End Speech Recognitioncited by 2
  5. 2021Data Incubation -- Synthesizing Missing Data for Handwriting Recognitioncited by 1
  6. 2021Towards Robust Waveform-Based Acoustic Modelscited by 1
  7. 2023Emphasizing Unseen Words: New Vocabulary Acquisition for End-to-End Speech Recognitioncited by 1
  8. 2024Contrastive Learning from Synthetic Audio Doppelg"angerscited by 1
  9. 2021Label-Synchronous Speech-to-Text Alignment for ASR Using Forward and Backward Transformerscited by 0
  10. 2022JoeyS2T: Minimalistic Speech-to-Text Modeling with JoeyNMTcited by 0
  11. 2023MAC: A unified framework boosting low resource automatic speech recognitioncited by 0
  12. 2023Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesiscited by 0
  13. 2024Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Researchcited by 0
  14. 2024Initial Decoding with Minimally Augmented Language Model for Improved Lattice Rescoring in Low Resource ASRcited by 0
  15. 2024Enhancing Synthetic Training Data for Speech Commands: From ASR-Based Filtering to Domain Adaptation in SSL Latent Spacecited by 0
  16. 2026Development and multi-center evaluation of domain-adapted speech recognition for human-AI teaming in real-world gastrointestinal endoscopycited by 0
  17. 2026Synthetic Speech, Real Signal: Paralinguistic Preservation and Cross-Lingual Augmentation via Voice Cloningcited by 0