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Art"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Speech is a highly coordinated process that requires precise control over vocal tract morphology\/motion to produce intelligible sounds while simultaneously generating unique exhaled flow patterns. The schlieren imaging technique visualizes airflows with subtle density variations. It is hypothesized that speech flows captured by schlieren, when analyzed using a hybrid of convolutional neural network (CNN) and long short-term memory (LSTM) network, can recognize alphabet pronunciations, thus facilitating automatic speech recognition and speech disorder therapy. This study evaluates the feasibility of using a CNN-based video classification network to differentiate speech flows corresponding to the first four alphabets: \/A\/, \/B\/, \/C\/, and \/D\/. A schlieren optical system was developed, and the speech flows of alphabet pronunciations were recorded for two participants at an acquisition rate of 60 frames per second. A total of 640 video clips, each lasting 1\u00a0s, were utilized to train and test a hybrid CNN-LSTM network. Acoustic analyses of the recorded sounds were conducted to understand the phonetic differences among the four alphabets. The hybrid CNN-LSTM network was trained separately on four datasets of varying sizes (i.e., 20, 30, 40, 50 videos per alphabet), all achieving over 95% accuracy in classifying videos of the same participant. However, the network\u2019s performance declined when tested on speech flows from a different participant, with accuracy dropping to around 44%, indicating significant inter-participant variability in alphabet pronunciation. Retraining the network with videos from both participants improved accuracy to 93% on the second participant. Analysis of misclassified videos indicated that factors such as low video quality and disproportional head size affected accuracy. These results highlight the potential of CNN-assisted speech recognition and speech therapy using articulation flows, although challenges remain in expanding the alphabet set and participant cohort.<\/jats:p>","DOI":"10.1186\/s42492-024-00163-w","type":"journal-article","created":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T03:38:30Z","timestamp":1716349110000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Schlieren imaging and video classification of alphabet pronunciations: exploiting phonetic flows for speech recognition and speech therapy"],"prefix":"10.1186","volume":"7","author":[{"given":"Mohamed","family":"Talaat","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kian","family":"Barari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuhua April","family":"Si","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6423-0759","authenticated-orcid":false,"given":"Jinxiang","family":"Xi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,22]]},"reference":[{"issue":"11","key":"163_CR1","doi-asserted-by":"publisher","first-page":"4112","DOI":"10.1044\/2022_jslhr-22-00177","volume":"65","author":"C Cordella","year":"2022","unstructured":"Cordella C, Gutz SE, Eshghi M, Stipancic KL, Schliep M, Dickerson BC et al (2022) Acoustic and kinematic assessment of motor speech impairment in patients with suspected four-repeat tauopathies. 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