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However, there is a lack of uniform and applicable methods for measuring the impact on speech production after treatment in this tumor location.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objective<\/jats:title><jats:p>The objective of this work is to (1) model an automatic severity index of speech applicable in clinical practice, that is equivalent or superior to a severity score obtained by human listeners, via several acoustics parameters extracted (a) directly from speech signal and (b) resulting from speech processing and (2) derive an automatic speech intelligibility classification (i.e., mild, moderate, severe) to predict speech disability and handicap by combining the listener comprehension score with self\u2010reported quality of life related to speech.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Eighty\u2010seven patients treated for cancer of the oral cavity or the oropharynx and 35 controls performed different tasks of speech production and completed questionnaires on speech\u2010related quality of life. The audio recordings were then evaluated by human perception and automatic speech processing. Then, a score was developed through a classic logistic regression model allowing description of the severity of patients' speech disorders.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Among the group of parameters subject to extraction from automatic processing of the speech signal, six were retained, producing a correlation at 0.87 with the perceptual reference score, 0.77 with the comprehension score, and 0.5 with speech\u2010related quality of life.<\/jats:p><jats:p>The parameters that contributed the most are based on automatic speech recognition systems. These are mainly the automatic average normalized likelihood score on a text reading task and the score of cumulative rankings on pseudowords. The reduced automatic YC2SI is modeled in this way: <jats:italic>Y<\/jats:italic><jats:sub>C2SIp<\/jats:sub>\u00a0=\u200911.48726\u2009+ (1.52926\u2009\u00d7\u00a0<jats:sub>Xaveraged normalized likelihood reading<\/jats:sub>)\u2009+\u2009(\u22121.94e\u201006\u2009\u00d7\u00a0<jats:sub>Xscore of cumulative ranks pseudowords<\/jats:sub>).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Automatic processing of speech makes it possible to arrive at valid, reliable, and reproducible parameters able to serve as references in the framework of follow\u2010up of patients treated for cancer of the oral cavity or the oropharynx.<\/jats:p><\/jats:sec>","DOI":"10.1002\/hed.26903","type":"journal-article","created":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T05:17:19Z","timestamp":1635916639000},"page":"71-88","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Construction of an automatic score 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