Z. Batzorig, Odsuren Bukhtsooj, A. Chensky, T. Galbaatar
{"title":"Speech Recognition in Mongolian Language using a Neural Network with pre-processing Technique","authors":"Z. Batzorig, Odsuren Bukhtsooj, A. Chensky, T. Galbaatar","doi":"10.1109/REEPE49198.2020.9059228","DOIUrl":null,"url":null,"abstract":"In this paper, we developed a neural network model, which is capable of recognizing a limited number of words in Mongolian language. We have chosen four words in Mongolian language. These words were chosen for further designing and creating a special device with an audio interface. In this experiment, we used audio recordings recorded in a computer using a microphone in a normal audience with minimal background noise. The database of audio recordings used to train the neural network consists of speeches of 11 people (7 men and 4 women). One of them is around 20–30 years old, three people are 60–70 and the rest are 30–40. The work uses a regular personal computer with an Intel Core i5 processor - the 3rd generation and with 8GB DDR IV RAM.","PeriodicalId":142369,"journal":{"name":"2020 International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/REEPE49198.2020.9059228","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
In this paper, we developed a neural network model, which is capable of recognizing a limited number of words in Mongolian language. We have chosen four words in Mongolian language. These words were chosen for further designing and creating a special device with an audio interface. In this experiment, we used audio recordings recorded in a computer using a microphone in a normal audience with minimal background noise. The database of audio recordings used to train the neural network consists of speeches of 11 people (7 men and 4 women). One of them is around 20–30 years old, three people are 60–70 and the rest are 30–40. The work uses a regular personal computer with an Intel Core i5 processor - the 3rd generation and with 8GB DDR IV RAM.