Phonetic-acoustic and feature analyses by a neural network to assess speech quality in patients treated for head and neck cancer

Subjective speech evaluation is the gold standard to assess speech quality of head and neck cancer patients. This study investigates if conventional acoustic-phonetic and novel feature analysis contribute to the development of a multidimensional speech assessment protocol. Speech recordings of 51 patients 6 months post-treatment and of 18 control speakers were subjectively evaluated for intelligibility, nasal resonance and articulation. Selfevaluation of speech problems was assessed by the EORTC QLQ-H&N35 speech subscale. Feature analysis was performed to assess objectively nasality in vowels /a,i,u/. Results revealed that size of the vowel triangle, pressure release of /k/ and nasality in /i/ predict best intelligibility, articulation and nasal resonance and differentiated best between patients and controls. Within patients, /k/ and /x/ differentiated tumour site and tumour classification. Various objective variables were related to speech problems as reported by patients.