Automatic selection of the most characterizing features for different types of atypical speech

Speech contains a lot of information about the speaker. Characteristics such as age, gender, accent and even health can be deduced by analysing someone’s spoken language. Since recording speech is a non-invasive and low-effort way to potentially analyse someone’s health, efforts have been made to create applications for remote monitoring or diagnosis for several pathologies that are expressed in speech. To ensure the accuracy of these applications, it is vital to find so-called markers in the speech that indicate whether the speech is atypical or typical. Any type of speech that deviates from typical speech by either being from a speaker who is not a native speaker, not an adult, or suffering from a speech disorder, is considered to be atypical speech in this thesis. Each type of atypical speech has its own markers that show the differences from typical speech. In this thesis, three different types of Dutch atypical speech are examined. Firstly, speech from Chronic Obstructive Pulmonary Disease (COPD) patients, while either experiencing a lung attack or being stable again, is analysed in comparison with each other as well as healthy speech. Secondly, speech from Parkinson’s Disease (PD) patients who participated in an experiment with a serious speech therapy game is analysed at three times: pre-experiment, pre-treatment, and post-treatment. By automatically computing a number of Praat and eGeMAPS features from speech on three different linguistic levels (full, word-segmented and phoneme-segmented), ranking these features with a Recursive Feature Elimination (RFE) method, classifying them with binary comparisons using Support Vector Machine (SVM) and other classifiers, and finally evaluating them using statistical tests, the aim of this thesis is to find the most characterizing features per type of atypical speech in Dutch. The top ten characteristic features were found to be significant in the multivariate tests for all three types of atypical speech, indicating that the combination of these features is indeed characteristic for that type of speech. These top ten features were inspected more closely and compared with characteristic features found in previous studies. Additional achievements This thesis has lead to two conference publications and three presentations, that are listed below. The two publications: van Bemmel L. , Harmsen W. , Cucchiarini C. , Strik H. (2021) Automatic Selection of the Most Characterizing Features for Detecting COPD in Speech. In: Karpov A. , Potapova R. (eds) Speech and Computer. SPECOM 2021. Lecture Notes in Computer Science, vol 12997. Springer, Cham. https://doi.org/10.1007/978-3-030-87802-3_66 van Bemmel L. , Cucchiarini C. , Strik H. (2021) Using feature selection to evaluate pathological speech after training with a serious game. In: ExLing 2021. https://doi.org/10.36505/ExLing-2021/12 The three presentations: Automatic Selection of the Most Characterizing Features for Detecting COPD in Speech at Combined PI Research Meeting (Centre for Language Studies, Radboud University Nijmegen) 26 May 2021 Automatic Selection of the Most Characterizing Features for Detecting COPD in Speech at 23rd International Conference on Speech and Computer (SPECOM) in the Computational Paralinguistics session 29 September 2021 Using feature selection to evaluate pathological speech after training with a serious game at 12th International Conference on Experimental Linguistics (ExLing) 12 October 2021