Automatic Prediction of Speech Intelligibility based on X-vectors in the context of Head and Neck Cancer

In the context of pathological speech, perceptual evaluation is still the most widely used method for intelligibility estimation. Despite being considered a staple in clinical settings, it has a well-known subjectivity associated with it, which results in greater variances and low reproducibility. On the other hand, due to the increasing computing power and latest research, automatic evaluation has become a growing alternative to perceptual assessments. In this paper we investigate an automatic prediction of speech intelligibility using the x-vector paradigm, in the context of head and neck cancer. Experimental evaluation of the proposed model suggests a high correlation rate when applied to our corpus of HNC patients (p = 0.85). Our approach also displayed the possibility of achieving very high correlation values (p = 0.95) when adapting the evaluation to each individual speaker, displaying a significantly more accurate prediction whilst using smaller amounts of data. These results can also provide valuable insight to the redevelopment of test protocols, which typically tend to be substantial and effort-intensive for patients.