{"created":"2021-01-26T07:20:46.516139+00:00","id":7793,"links":{},"metadata":{"_buckets":{"deposit":"2aa644bb-a79c-4303-be73-926765eb99a7"},"_deposit":{"created_by":73,"id":"7793","owner":"73","owners":[73],"owners_ext":{"displayname":"","email":"thandar_htwe@miit.edu.mm","username":""},"pid":{"revision_id":0,"type":"depid","value":"7793"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/00007793","sets":["1582963674932","1582963674932:1597396989070"]},"communities":["miit"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Continuous Speech Recognition System Based on Deep Convolutional  Neural Network for Myanmar","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"Automatic  Speech  Recognition  (ASR)  system, that translates the speech signal into text words, is still a challenge  in  the  continuous  speech  signal.  Continuous speech  recognition  systems  develop  with  four  separated \nsegmentation \nspeech    signal,    feature \nof \nthe \nsteps: \nextraction, classification and recognizing the words. These steps can be modeled with the various methods. Among them,  the  combination  model  of  the  dynamic  threshold based segmentation, Mel-Frequency Cepstral Coefficient \n(MFCC)     feature     extraction     method \nDeep \nand \nConvolutional Neural Network (DCNN) is proposed in this paper.  Especially,  DCNN-AlexNet  has  been  applied  in image  processing  because  it  can  perform  as  a  highly accurate, effective and powerful classifier. In the training and classification step of this system, the advantages of DCNN in image processing are applied using the MFCC feature  images.  The  main  purpose of  this  system  is  to transform the  MFCC  features  of  the  speech  signal  to MFCC features images with various frame size for three layers of input images of DCNN. The three layers 32*32*3 images are used for the input images of DCNN-AlexNet to \nrecognition \nof \nthe \nthe \nThe \nsystem."}]},"item_1583103108160":{"attribute_name":"Keywords","attribute_value_mlt":[{"interim":"Automatic Speech Recognition, Mel-Frequency Cepstral Coefficient, Deep Convolutional Neural Network, Word Error Rate"}]},"item_1583103120197":{"attribute_name":"Files","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2021-01-26"}],"displaytype":"preview","filename":"ICSTSD_2018  revised paper.pdf","filesize":[{"value":"510 KB"}],"format":"application/pdf","licensetype":"license_3","url":{"url":"https://meral.edu.mm/record/7793/files/ICSTSD_2018  revised paper.pdf"},"version_id":"405bf328-4191-443c-8fa8-ca8d5e49695d"}]},"item_1583103147082":{"attribute_name":"Conference papers","attribute_value_mlt":[{"subitem_acronym":"ICSTSD","subitem_c_date":"2018-05-14","subitem_conference_title":"Proc. of  1st Intl. Conf. on Science and Technology for Sustainable Development (ICSTSD 2018)","subitem_place":"UCSY, Myanmar","subitem_website":"www.ucsy.edu.mm"}]},"item_1583105942107":{"attribute_name":"Authors","attribute_value_mlt":[{"subitem_authors":[{"subitem_authors_fullname":"Yin Win Chit"},{"subitem_authors_fullname":"Soe Soe Khaing"},{"subitem_authors_fullname":"Yi Yi Myint"}]}]},"item_1583108359239":{"attribute_name":"Upload type","attribute_value_mlt":[{"interim":"Publication"}]},"item_1583108428133":{"attribute_name":"Publication type","attribute_value_mlt":[{"interim":"Conference paper"}]},"item_1583159729339":{"attribute_name":"Publication date","attribute_value":"2018-05-14"},"item_title":"Continuous Speech Recognition System Based on Deep Convolutional  Neural Network for Myanmar","item_type_id":"21","owner":"73","path":["1582963674932","1597396989070"],"publish_date":"2018-05-14","publish_status":"0","recid":"7793","relation_version_is_last":true,"title":["Continuous Speech Recognition System Based on Deep Convolutional  Neural Network for Myanmar"],"weko_creator_id":"73","weko_shared_id":-1},"updated":"2021-12-13T06:59:38.965651+00:00"}