Acoustic-phonetic and artificial neural network feature analysis to assess speech quality of stop consonants produced by patients treated for oral or oropharyngeal cancer

Speech impairment often occurs in patients after treatment for head and neck cancer. A specific speech characteristic that influences intelligibility and speech quality is voice-onset-time (VOT) in stop consonants. VOT is one of the functionally most relevant parameters that distinguishes voiced and voiceless stops. The goal of the present study is to investigate the role and validity of acoustic-phonetic and artificial neural network analysis (ANN) of stop consonants in a multidimensional speech assessment protocol. Speech recordings of 51 patients 6 months after treatment for oral or oropharyngeal cancer and of 18 control speakers were evaluated by trained speech pathologists regarding intelligibility and articulation. Acoustic-phonetic analyses and artificial neural network analysis of the phonological feature voicing were performed in voiced /b/, /d/ and voiceless /p/ and /t/. Results revealed that objective acoustic-phonetic analysis and feature analysis for /b, d, p/ distinguish between patients and controls. Within patients, /t, d/ distinguish for tumour location and tumour stage. Measurements of the phonological feature voicing in almost all consonants were significantly correlated with articulation and intelligibility, but not with self-evaluations. Overall, objective acoustic-phonetic and feature analyses of stop consonants are feasible and contribute to further development of a multidimensional speech quality assessment protocol. © 2011 Elsevier B.V. All rights reserved.