# Disentangled Latent Speech Representation for Automatic Pathological Intelligibility Assessment

arXiv: https://arxiv.org/abs/2204.04016 · HTML: https://mlsanity.com/paper/2204.04016/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 8 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Unsupervised Speech Decomposition via Triple Information Bottleneck](https://mlsanity.com/paper/2004.11284/) (2020)
- [Common Phone: A Multilingual Dataset for Robust Acoustic Modelling](https://mlsanity.com/paper/2201.05912/) (2022)
- [Speech Disorder Classification Using Extended Factorized Hierarchical Variational Auto-encoders](https://mlsanity.com/paper/2106.07337/) (2021)

## Abstract (arXiv)

Speech intelligibility assessment plays an important role in the therapy of patients suffering from pathological speech disorders. Automatic and objective measures are desirable to assist therapists in their traditionally subjective and labor-intensive assessments. In this work, we investigate a novel approach for obtaining such a measure using the divergence in disentangled latent speech representations of a parallel utterance pair, obtained from a healthy reference and a pathological speaker. Experiments on an English database of Cerebral Palsy patients, using all available utterances per speaker, show high and significant correlation values (R = -0.9) with subjective intelligibility measures, while having only minimal deviation (+-0.01) across four different reference speaker pairs. We also demonstrate the robustness of the proposed method (R = -0.89 deviating +-0.02 over 1000 iterations) by considering a significantly smaller amount of utterances per speaker. Our results are among the first to show that disentangled speech representations can be used for automatic pathological speech intelligibility assessment, resulting in a reference speaker pair invariant method, applicable in scenarios with only few utterances available.