Thu-3-9-7 Automatic Prediction of Speech Intelligibility based on X-vectors in the context of Head and Neck Cancer

Abstract: 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 H&N 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.