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

  • Authors:

  • Marieke J. de Bruijn;Louis ten Bosch;Dirk J. Kuik;Birgit I. Witte;Johannes A. Langendijk;C. René Leemans;Irma M. Verdonck-de Leeuw

  • Affiliations:

  • Department of Otolaryngology-Head and Neck Surgery, VU University Medical Center, Amsterdam, The Netherlands;Department of Language and Speech, University of Nijmegen, Nijmegen, The Netherlands;Department of Epidemiology & Biostatistics, VU University Medical Center, Amsterdam, The Netherlands;Department of Epidemiology & Biostatistics, VU University Medical Center, Amsterdam, The Netherlands;Department of Radiation Oncology, University of Groningen Medical Center, University of Groningen, Groningen, The Netherlands;Department of Otolaryngology-Head and Neck Surgery, VU University Medical Center, Amsterdam, The Netherlands;Department of Otolaryngology-Head and Neck Surgery, VU University Medical Center, Amsterdam, The Netherlands

  • Venue:
  • Speech Communication
  • Year:
  • 2012

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Abstract

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 6months 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.