{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T03:08:58Z","timestamp":1648523338457},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T00:00:00Z","timestamp":1606089600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,11,23]]},"abstract":"<jats:p>The goal of this paper is to present a word-final target phoneme automated segmentation method based on cross-correlation coefficients computed between a reference sound wave and a sample sound wave. Most existing Speech Sound Disorder (SSD) Screening solutions require human intervention to a greater or lesser extent and use segmentation methods based on hard-coded time frames. Moreover, existing solutions extract features from the frequency domain, which entails large amounts of computational power to the detriment of real-time feedback. The pre-processing algorithm proposed in this paper, implemented in a Python version 3.7 script, automatically generates 2 new .wav files corresponding to the phonemes found in word-final position in the initial sound waves. The newly-generated .wav files are meant to be used as valid and homogeneous input in a subsequent classification stage aimed at rigorously discriminating mispronunciations of the target phoneme and assist Speech-Language Pathologists (SLPs) with the SSD screening.<\/jats:p>","DOI":"10.3233\/shti200709","type":"book-chapter","created":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T23:39:43Z","timestamp":1606174783000},"source":"Crossref","is-referenced-by-count":0,"title":["Word-Final Phoneme Segmentation Using Cross-Correlation"],"prefix":"10.3233","author":[{"given":"Emilian-Erman","family":"Mahmut","sequence":"first","affiliation":[{"name":"Department of Automation and Applied Informatics, Politehnica University Timisoara, Romania"}]},{"given":"Stelian","family":"Nicola","sequence":"additional","affiliation":[{"name":"Department of Automation and Applied Informatics, Politehnica University Timisoara, Romania"}]},{"given":"Vasile","family":"Stoicu-Tivadar","sequence":"additional","affiliation":[{"name":"Department of Automation and Applied Informatics, Politehnica University Timisoara, Romania"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Integrated Citizen Centered Digital Health and Social Care"],"original-title":[],"link":[{"URL":"http:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI200709","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T02:49:32Z","timestamp":1606272572000},"score":1,"resource":{"primary":{"URL":"http:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI200709"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,23]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti200709","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,23]]}}}