{"title":"深度神经网络在实时语音命令识别中的应用","authors":"Ihor Mykhailichenko, Heorhii Ivashchenko, Olesia Barkovska, Oleksii Liashenko","doi":"10.1109/KhPIWeek57572.2022.9916473","DOIUrl":null,"url":null,"abstract":"The article proposes a method for voice command recognition in real time mode using a deep neural network. which is built with a combination of different artificial neural networks architectures. Pre-processing of the sound signal is used for further recognition. The evaluation of the recognition efficiency is carried out by means of a comparative analysis. The results of experimental studies (voice recognition accuracy of 85%) demonstrate the features of the proposed method.","PeriodicalId":197096,"journal":{"name":"2022 IEEE 3rd KhPI Week on Advanced Technology (KhPIWeek)","volume":"275 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Application of Deep Neural Network for Real-Time Voice Command Recognition\",\"authors\":\"Ihor Mykhailichenko, Heorhii Ivashchenko, Olesia Barkovska, Oleksii Liashenko\",\"doi\":\"10.1109/KhPIWeek57572.2022.9916473\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The article proposes a method for voice command recognition in real time mode using a deep neural network. which is built with a combination of different artificial neural networks architectures. Pre-processing of the sound signal is used for further recognition. The evaluation of the recognition efficiency is carried out by means of a comparative analysis. The results of experimental studies (voice recognition accuracy of 85%) demonstrate the features of the proposed method.\",\"PeriodicalId\":197096,\"journal\":{\"name\":\"2022 IEEE 3rd KhPI Week on Advanced Technology (KhPIWeek)\",\"volume\":\"275 \",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 3rd KhPI Week on Advanced Technology (KhPIWeek)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/KhPIWeek57572.2022.9916473\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 3rd KhPI Week on Advanced Technology (KhPIWeek)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KhPIWeek57572.2022.9916473","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of Deep Neural Network for Real-Time Voice Command Recognition
The article proposes a method for voice command recognition in real time mode using a deep neural network. which is built with a combination of different artificial neural networks architectures. Pre-processing of the sound signal is used for further recognition. The evaluation of the recognition efficiency is carried out by means of a comparative analysis. The results of experimental studies (voice recognition accuracy of 85%) demonstrate the features of the proposed method.