Publication:
The optimal algorithm of sub-symptom threshold exercise training for aural habilitation/rehabilitation

dc.contributor.authorSueaseenak D.
dc.contributor.authorSangsai P.
dc.contributor.authorDetyong P.
dc.date.accessioned2021-04-05T03:21:58Z
dc.date.available2021-04-05T03:21:58Z
dc.date.issued2017
dc.date.issuedBE2560
dc.description.abstractThis paper presents the comparison study of the speech recognition system for the Thai language in the noise of the different environment. The well-known algorithms, such as MLP, SVM, GMM, HMM, VQ, DTW, DNN and End to End were used in this research. A test was conduced with 50 men and 50 women subjects during 5-60 years old. The proposed method consists of several parts which are (i) the feature extraction by Mel-frequency cepstral coefficients (MFCC) algorithm, (ii) The learning and decision process. The performance testing of the systems by the Ling's six sounds, such as ah, mm, oo, ee, sh and ss. The experiment results of our proposed method show that the accuracy of the system more than 80 percent. © 2017 Association for Computing Machinery.
dc.format.mimetypeapplication/pdf
dc.identifier.citationACM International Conference Proceeding Series. (2017), p.48-51
dc.identifier.doi10.1145/3168776.3168782
dc.identifier.other2-s2.0-85041894823
dc.identifier.urihttps://hdl.handle.net/20.500.14740/4011
dc.rights.holderมหาวิทยาลัยศรีนครินทรวิโรฒ
dc.subject.otherBioinformatics
dc.subject.otherCharacter recognition
dc.subject.otherComparison study
dc.subject.otherDecision process
dc.subject.otherExercise training
dc.subject.otherMel-frequency cepstral coefficients
dc.subject.otherOptimal algorithm
dc.subject.otherPerformance testing
dc.subject.otherSpeech recognition systems
dc.subject.otherThai language
dc.subject.otherSpeech recognition
dc.titleThe optimal algorithm of sub-symptom threshold exercise training for aural habilitation/rehabilitation
dc.typeConference Paper
dspace.entity.typePublication
swu.datasource.scopushttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85041894823&doi=10.1145%2f3168776.3168782&partnerID=40&md5=e45c0310f32c9076fb997352fd2e6854

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