The incidence of penetration or aspiration according to VFSS is high in this cohort of patients with TCSCI. Therefore, the swallowing function of patients with acute TCSCI should be routinely evaluated before initiating oral feeding. VFSS is highly recommended, particularly to rule out the possibility of silent aspiration and to achieve information on safe nutrition consistency.

The necessity of bronchoscopies, postinjury lower cervical spine anterior surgery, coughing, throat clearing, choking, and changes in voice quality related to swallowing was a markedrisk factor for aspiration and penetration following a cervical SCI. These factors and signs should be used to suspect injury-related pharyngeal dysfunction and to initiate preventive measures to avoid complications. The clinical swallowing evaluation is a relevant adjunct in the management of these patients and can improve the detection of penetration and aspiration.

Dysarthria is a common symptom for people with Parkinson's disease (PD), which affects respiration, phonation, articulation and prosody, and reduces the speech intelligibility as a result. Imprecise vowel articulation can be observed in people with PD. Acoustic features measuring vowel articulation have been demonstrated to be effective indicators of PD in its detection and assessment. Standard clinical vowel articulation features include the first two formants of the three corner vowels /a/, /i/ and /u/, from which clinically relevant parameters such as vowel working space area (VSA), vowel articulation index (VAI) and formants centralization ratio (FCR) are derived. Conventionally, manual annotation of the corner vowels from speech data is required before measuring vowel articulation. This process is time-consuming and requires specific expertise in speech signal analysis. The present work aims to reduce human effort in clinical analysis of PD speech by proposing an automatic pipeline for vowel articulation assessment. The method is based on automatic corner vowel detection using a language universal phoneme recognizer, followed by statistical analysis of the formant data. The approach removes the restrictions of prior knowledge of speaking content and the language in question. Experimental results on a Finnish PD speech corpus demonstrate the efficacy and reliability of the proposed method in deriving VAI, VSA, FCR and F2i/F2u (the second formant ratio for vowels /i/ and /u/) scores in a fully automated manner. The automatically computed parameters are shown to be highly correlated with features computed with manual annotations of corner vowels. In addition, automatically and manually computed vowel articulation features have comparable correlations with experts' ratings on speech intelligibility, voice impairment and overall severity of communication disorder. Language-independence of the proposed approach is further validated on a Spanish PD database, PC-GITA, as well as on TORGO corpus of English dysarthric speech. Results from these two corpora further demonstrate the efficacy of the automated features in separating PD/dysarthric speakers from controls, and that the features are correlated with Parkinson's disease severity ratings on PC-GITA and with level of dysarthria on TORGO.