mlsanityData to 7 Oct 2026

Non Intrusive Intelligibility Predictor for Hearing Impaired Individuals using Self Supervised Speech Representations

arXiv 2307.13423 ↗First posted 25 Jul 2023cs.SD cs.LG eess.AScited by 3

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Abstract (arXiv)

Self-supervised speech representations (SSSRs) have been successfully applied to a number of speech-processing tasks, e.g. as feature extractor for speech quality (SQ) prediction, which is, in turn, relevant for assessment and training speech enhancement systems for users with normal or impaired hearing. However, exact knowledge of why and how quality-related information is encoded well in such representations remains poorly understood. In this work, techniques for non-intrusive prediction of SQ ratings are extended to the prediction of intelligibility for hearing-impaired users. It is found that self-supervised representations are useful as input features to non-intrusive prediction models, achieving competitive performance to more complex systems. A detailed analysis of the performance depending on Clarity Prediction Challenge 1 listeners and enhancement systems indicates that more data might be needed to allow generalisation to unknown systems and (hearing-impaired) individuals

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 20 of its 33 references that are in the map. First posted on arXiv 25 Jul 2023.

Builds on

20 of 33 references.

References · 20

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8 more
  1. 2022MBI-Net: A Non-Intrusive Multi-Branched Speech Intelligibility Prediction Model for Hearing Aidscited by 11
  2. 2023Perceive and predict: self-supervised speech representation based loss functions for speech enhancementcited by 8
  3. 2022Pre-trained Speech Representations as Feature Extractors for Speech Quality Assessment in Online Conferencing Applicationscited by 7
  4. 2022MetricGAN+/-: Increasing Robustness of Noise Reduction on Unseen Datacited by 5
  5. 2022Exploring the influence of fine-tuning data on wav2vec 2.0 model for blind speech quality predictioncited by 5
  6. 2022Unsupervised Uncertainty Measures of Automatic Speech Recognition for Non-intrusive Speech Intelligibility Predictioncited by 5
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  8. 2021Non-Intrusive Binaural Speech Intelligibility Prediction from Discrete Latent Representationscited by 1

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Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Non-Intrusive Speech Intelligibility Prediction for Hearing-Impaired Users using Intermediate ASR Features and Human Memory Modelscited by 9
  2. 2024Speech foundation models on intelligibility prediction for hearing-impaired listenerscited by 6
  3. 2025WhiSQA: Non-Intrusive Speech Quality Prediction Using Whisper Encoder Featurescited by 1