Automated detection of unfilled pauses in speech of healthy and brain-damaged individuals

Auteurs Fedor Jalvingh , Roel Jonkers , Roelant Ossewaarde , Yvonne Bastiaanse
Gepubliceerd in Proceedings
Publicatiedatum 27 oktober 2017
Lectoraat Artificial Intelligence
Soort publicatie Lezing


Conference paper - 5th International Conference on Statistical Language and Speech Processing SLSP 2017. Pauses in speech may be categorized on the basis of their length. Some authors claim that there are two categories (short and long pauses) (Baken & Orlikoff, 2000), others claim that there are three (Campione & Véronis, 2002), or even more. Pause lengths may be affected in speakers with aphasia. Individuals with dementia probably caused by Alzheimer’s disease (AD) or Parkinson’s disease (PD) interrupt speech longer and more frequently. One infrequent form of dementia, non-fluent primary progressive aphasia (PPA-NF), is even defined as causing speech with an unusual interruption pattern (”hesitant and labored speech”). Although human listeners can often easily distinguish pathological speech from healthy speech, it is unclear yet how software can detect the relevant patterns. The research question in this study is: how can software measure the statistical parameters that characterize the disfluent speech of PPA-NF/AD/PD patients in connected conversational speech?

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Taal Engels
Gepubliceerd in Proceedings
Trefwoorden Spraakvermogen, Taalvermogen, Dementie

Artificial Intelligence