Matches in SemOpenAlex for { <https://semopenalex.org/work/W4296105462> ?p ?o ?g. }
- W4296105462 endingPage "6966" @default.
- W4296105462 startingPage "6966" @default.
- W4296105462 abstract "Aphasia is a type of speech disorder that can cause speech defects in a person. Identifying the severity level of the aphasia patient is critical for the rehabilitation process. In this research, we identify ten aphasia severity levels motivated by specific speech therapies based on the presence or absence of identified characteristics in aphasic speech in order to give more specific treatment to the patient. In the aphasia severity level classification process, we experiment on different speech feature extraction techniques, lengths of input audio samples, and machine learning classifiers toward classification performance. Aphasic speech is required to be sensed by an audio sensor and then recorded and divided into audio frames and passed through an audio feature extractor before feeding into the machine learning classifier. According to the results, the mel frequency cepstral coefficient (MFCC) is the most suitable audio feature extraction method for the aphasic speech level classification process, as it outperformed the classification performance of all mel-spectrogram, chroma, and zero crossing rates by a large margin. Furthermore, the classification performance is higher when 20 s audio samples are used compared with 10 s chunks, even though the performance gap is narrow. Finally, the deep neural network approach resulted in the best classification performance, which was slightly better than both K-nearest neighbor (KNN) and random forest classifiers, and it was significantly better than decision tree algorithms. Therefore, the study shows that aphasia level classification can be completed with accuracy, precision, recall, and F1-score values of 0.99 using MFCC for 20 s audio samples using the deep neural network approach in order to recommend corresponding speech therapy for the identified level. A web application was developed for English-speaking aphasia patients to self-diagnose the severity level and engage in speech therapies." @default.
- W4296105462 created "2022-09-17" @default.
- W4296105462 creator A5002072930 @default.
- W4296105462 creator A5011075158 @default.
- W4296105462 creator A5012202651 @default.
- W4296105462 creator A5050585435 @default.
- W4296105462 creator A5077108129 @default.
- W4296105462 creator A5079696428 @default.
- W4296105462 date "2022-09-14" @default.
- W4296105462 modified "2023-10-17" @default.
- W4296105462 title "Automatic Assessment of Aphasic Speech Sensed by Audio Sensors for Classification into Aphasia Severity Levels to Recommend Speech Therapies" @default.
- W4296105462 cites W1774348980 @default.
- W4296105462 cites W1959209385 @default.
- W4296105462 cites W1968408912 @default.
- W4296105462 cites W1969824057 @default.
- W4296105462 cites W1978280226 @default.
- W4296105462 cites W1984091377 @default.
- W4296105462 cites W1988630590 @default.
- W4296105462 cites W1988871841 @default.
- W4296105462 cites W1990926279 @default.
- W4296105462 cites W1999318234 @default.
- W4296105462 cites W2007191692 @default.
- W4296105462 cites W2013728074 @default.
- W4296105462 cites W2015033691 @default.
- W4296105462 cites W2017100743 @default.
- W4296105462 cites W2028190691 @default.
- W4296105462 cites W2035269720 @default.
- W4296105462 cites W2037760741 @default.
- W4296105462 cites W2044133885 @default.
- W4296105462 cites W2062520179 @default.
- W4296105462 cites W2063042856 @default.
- W4296105462 cites W2064218608 @default.
- W4296105462 cites W2084777715 @default.
- W4296105462 cites W2094305626 @default.
- W4296105462 cites W2097222640 @default.
- W4296105462 cites W2099440903 @default.
- W4296105462 cites W2099493488 @default.
- W4296105462 cites W2112218471 @default.
- W4296105462 cites W2118379083 @default.
- W4296105462 cites W2121282288 @default.
- W4296105462 cites W2121639099 @default.
- W4296105462 cites W2126167570 @default.
- W4296105462 cites W2139438474 @default.
- W4296105462 cites W2142127073 @default.
- W4296105462 cites W2148424765 @default.
- W4296105462 cites W2152563247 @default.
- W4296105462 cites W2160815625 @default.
- W4296105462 cites W2250982388 @default.
- W4296105462 cites W2404317780 @default.
- W4296105462 cites W2439110883 @default.
- W4296105462 cites W2657631929 @default.
- W4296105462 cites W2747942190 @default.
- W4296105462 cites W2799525243 @default.
- W4296105462 cites W2889294685 @default.
- W4296105462 cites W2907121074 @default.
- W4296105462 cites W2909400275 @default.
- W4296105462 cites W2910712032 @default.
- W4296105462 cites W2913378418 @default.
- W4296105462 cites W2952708169 @default.
- W4296105462 cites W2961302496 @default.
- W4296105462 cites W2964243274 @default.
- W4296105462 cites W2970737019 @default.
- W4296105462 cites W2980398362 @default.
- W4296105462 cites W2989249010 @default.
- W4296105462 cites W3021970402 @default.
- W4296105462 cites W3027101363 @default.
- W4296105462 cites W3035035250 @default.
- W4296105462 cites W3042710331 @default.
- W4296105462 cites W3044885756 @default.
- W4296105462 cites W3045355365 @default.
- W4296105462 cites W3093285665 @default.
- W4296105462 cites W3097166469 @default.
- W4296105462 cites W3100732527 @default.
- W4296105462 cites W3114303312 @default.
- W4296105462 cites W3119573264 @default.
- W4296105462 cites W3133830507 @default.
- W4296105462 cites W3148181069 @default.
- W4296105462 cites W3149523164 @default.
- W4296105462 cites W3174221756 @default.
- W4296105462 cites W3201279130 @default.
- W4296105462 cites W3202568147 @default.
- W4296105462 cites W3203147359 @default.
- W4296105462 cites W4200170981 @default.
- W4296105462 cites W4200580203 @default.
- W4296105462 cites W852091371 @default.
- W4296105462 doi "https://doi.org/10.3390/s22186966" @default.
- W4296105462 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36146316" @default.
- W4296105462 hasPublicationYear "2022" @default.
- W4296105462 type Work @default.
- W4296105462 citedByCount "6" @default.
- W4296105462 countsByYear W42961054622023 @default.
- W4296105462 crossrefType "journal-article" @default.
- W4296105462 hasAuthorship W4296105462A5002072930 @default.
- W4296105462 hasAuthorship W4296105462A5011075158 @default.
- W4296105462 hasAuthorship W4296105462A5012202651 @default.
- W4296105462 hasAuthorship W4296105462A5050585435 @default.
- W4296105462 hasAuthorship W4296105462A5077108129 @default.
- W4296105462 hasAuthorship W4296105462A5079696428 @default.