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Training Neural Speech Recognition Systems with Synthetic Speech Augmentation

arXiv 1811.00707 ↗First posted 2 Nov 2018cs.CL cs.LG cs.SDcited by 21

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Abstract (arXiv)

Building an accurate automatic speech recognition (ASR) system requires a large dataset that contains many hours of labeled speech samples produced by a diverse set of speakers. The lack of such open free datasets is one of the main issues preventing advancements in ASR research. To address this problem, we propose to augment a natural speech dataset with synthetic speech. We train very large end-to-end neural speech recognition models using the LibriSpeech dataset augmented with synthetic speech. These new models achieve state of the art Word Error Rate (WER) for character-level based models without an external language model.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Nov 2018.

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Citing papers per year: 2021: 2; 2022: 7; 2023: 9; 2024: 3.

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  1. 2022Self-Supervised Speech Representation Learning: A Reviewcited by 260
  2. 2023StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learnerscited by 177
  3. 2021Injecting Text in Self-Supervised Speech Pretrainingcited by 24
  4. 2023GPT-FL: Generative Pre-trained Model-Assisted Federated Learningcited by 24
  5. 2022Differentially Private Speaker Anonymizationcited by 16
  6. 2023Text-only domain adaptation for end-to-end ASR using integrated text-to-mel-spectrogram generatorcited by 11
  7. 2022Synthesizing Dysarthric Speech Using Multi-talker TTS for Dysarthric Speech Recognitioncited by 10
  8. 2022ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversioncited by 10
  9. 2022Evaluating and reducing the distance between synthetic and real speech distributionscited by 9
  10. 2022When Is TTS Augmentation Through a Pivot Language Useful?cited by 8
  11. 2023Accurate synthesis of Dysarthric Speech for ASR data augmentationcited by 7
  12. 2023OLISIA: a Cascade System for Spoken Dialogue State Trackingcited by 5
9 more
  1. 2024AMuSeD: An Attentive Deep Neural Network for Multimodal Sarcasm Detection Incorporating Bi-modal Data Augmentationcited by 5
  2. 2022End-to-End Spoken Language Understanding: Performance analyses of a voice command task in a low resource settingcited by 3
  3. 2023SpeeChain: A Speech Toolkit for Large-Scale Machine Speech Chaincited by 2
  4. 2021Data Incubation -- Synthesizing Missing Data for Handwriting Recognitioncited by 1
  5. 2023Dialect Adaptation and Data Augmentation for Low-Resource ASR: TalTech Systems for the MADASR 2023 Challengecited by 1
  6. 2023MAC: A unified framework boosting low resource automatic speech recognitioncited by 0
  7. 2023AraSpot: Arabic Spoken Command Spottingcited by 0
  8. 2024Initial Decoding with Minimally Augmented Language Model for Improved Lattice Rescoring in Low Resource ASRcited by 0
  9. 2024Beyond Oversmoothing: Evaluating DDPM and MSE for Scalable Speech Synthesis in ASRcited by 0