mlsanityData to 7 Oct 2026

Speech Corpora Divergence Based Unsupervised Data Selection for ASR

arXiv 2302.13222 ↗First posted 26 Feb 2023cs.CL cs.SD eess.AScited by 0

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Abstract (arXiv)

Selecting application scenarios matching data is important for the automatic speech recognition (ASR) training, but it is difficult to measure the matching degree of the training corpus. This study proposes a unsupervised target-aware data selection method based on speech corpora divergence (SCD), which can measure the similarity between two speech corpora. We first use the self-supervised Hubert model to discretize the speech corpora into label sequence and calculate the N-gram probability distribution. Then we calculate the Kullback-Leibler divergence between the N-grams as the SCD. Finally, we can choose the subset which has minimum SCD to the target corpus for annotation and training. Compared to previous data selection method, the SCD data selection method can focus on more acoustic details and guarantee the diversity of the selected set. We evaluate our method on different accents from Common Voice. Experiments show that the proposed SCD data selection can realize 14.8% relative improvements to the random selection, comparable or even superior to the result of supervised selection.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 32 references that are in the map. First posted on arXiv 26 Feb 2023.

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References · 17

  1. 2017Neural Discrete Representation Learningcited by 4,650
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  3. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,301
  4. 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,358
  5. 2021Generative Spoken Language Modeling from Raw Audiocited by 336
  6. 2019vq-wav2vec: Self-Supervised Learning of Discrete Speech Representationscited by 335
  7. 2021Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Trainingcited by 148
  8. 2021Direct speech-to-speech translation with discrete unitscited by 148
  9. 2020Rethinking Evaluation in ASR: Are Our Models Robust Enough?cited by 72
  10. 2021Large-Scale Pre-Training of End-to-End Multi-Talker ASR for Meeting Transcription with Single Distant Microphonecited by 33
  11. 2022textless-lib: a Library for Textless Spoken Language Processingcited by 28
  12. 2022Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Datacited by 15
5 more
  1. 2022Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Datacited by 12
  2. 2022Sentence-Select: Large-Scale Language Model Data Selection for Rare-Word Speech Recognitioncited by 10
  3. 2022Unsupervised Data Selection via Discrete Speech Representation for ASRcited by 10
  4. 2022Unsupervised data selection for Speech Recognition with contrastive loss ratioscited by 7
  5. 2022Joint Encoder-Decoder Self-Supervised Pre-training for ASRcited by 4

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