Towards Reducing Patient Effort for the Automatic Prediction of Speech Intelligibility in Head and Neck Cancers

The automatic prediction of speech intelligibility can be seen as a growing and relevant alternative to the perceptual evaluations used clinically, which are known to be biased, variant and subjective. We propose an automatic way to regress an intelligibility score based on a recurrent model with a self-attention mechanism. This approach not only presented a high correlation of 0.87 when applied to a pseudo-word task designed for head and neck cancers, but also a significant decrease in error of more than 50%, when compared to previous approaches. Moreover, we have also studied the reliability of the same system when operating with smaller amounts of data at inference time. The results suggest that we can reduce the linguistic sample size to only 30% of the full sample, without losing performance. This aspect validates the reliability of using a smaller subset of data when predicting intelligibility, which can be extremely useful to prevent patient's fatigue by creating smaller batteries of clinical exams.