mlsanityData to 7 Oct 2026

Children's Speech Recognition through Discrete Token Enhancement

arXiv 2406.13431 ↗First posted 19 Jun 2024cs.CL cs.SD eess.AScited by 3

Not on the map. Why?

Abstract (arXiv)

Children's speech recognition is considered a low-resource task mainly due to the lack of publicly available data. There are several reasons for such data scarcity, including expensive data collection and annotation processes, and data privacy, among others. Transforming speech signals into discrete tokens that do not carry sensitive information but capture both linguistic and acoustic information could be a solution for privacy concerns. In this study, we investigate the integration of discrete speech tokens into children's speech recognition systems as input without significantly degrading the ASR performance. Additionally, we explored single-view and multi-view strategies for creating these discrete labels. Furthermore, we tested the models for generalization capabilities with unseen domain and nativity dataset. Results reveal that the discrete token ASR for children achieves nearly equivalent performance with an approximate 83% reduction in parameters.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 19 of its 36 references that are in the map. First posted on arXiv 19 Jun 2024.

Builds on

19 of 36 references.

References · 19

  1. 2017Attention Is All You Needcited by 59,932
  2. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  3. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  4. 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,609
  5. 2018Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidatescited by 632
  6. 2018ESPnet: End-to-End Speech Processing Toolkitcited by 554
  7. 2022Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understandingcited by 90
  8. 2022E-Branchformer: Branchformer with Enhanced merging for speech recognitioncited by 90
  9. 2023Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Studycited by 42
  10. 2023BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language modelscited by 29
  11. 2023Exploration of Efficient End-to-End ASR using Discretized Input from Self-Supervised Learningcited by 28
  12. 2023Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech Recognition for Children VS. Adultscited by 26
7 more
  1. 2023My Science Tutor (MyST) -- A Large Corpus of Children's Conversational Speechcited by 26
  2. 2023Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASRcited by 23
  3. 2022DRAFT: A Novel Framework to Reduce Domain Shifting in Self-supervised Learning and Its Application to Children's ASRcited by 21
  4. 2022A Wav2vec2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech Recognitioncited by 15
  5. 2020Analysis of Disfluency in Children's Speechcited by 6
  6. 2021Low Resource German ASR with Untranscribed Data Spoken by Non-native Children -- INTERSPEECH 2021 Shared Task SPAPL Systemcited by 4
  7. 2023Robust Self Supervised Speech Embeddings for Child-Adult Classification in Interactions involving Children with Autismcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Can Layer-wise SSL Features Improve Zero-Shot ASR Performance for Children's Speech?cited by 1
  2. 2026Cross-Dataset, Age, and Gender Generalization: A Comprehensive Analysis of Fine-Tuning Strategies for Low-Resource Children's ASRcited by 0
  3. 2026Light-weight Pronunciation Assessment via Discrete Speech Token Surprisalcited by 0