{"title":"一种采用LPC-ANFIS语音识别的直流电机调速控制系统","authors":"M. Akil, I. Nurtanio, R. Sadjad","doi":"10.1109/QIR.2017.8168484","DOIUrl":null,"url":null,"abstract":"The aim of this research is to design an implementation of the speech recognition system to control the speed of a DC motor. The Linear Predictive Coding (LPC) method is used in the speed recognition system, tuned by the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) method. There are 5 (five) samples of voice signals in Bahasa Indonesia recognized by this system, i.e.: “Nyala”, “Lambat”, “Sedang”, “Cepat” and “Mati”. Every voice signal is repeated 5 (five) times until as many as 25 samples are recorded. Their voice characteristics are extracted using the LPC method represented by the LPC coefficients stored in a database system. The ANFIS method is implemented in 50 iterations to tune and to train the LPC coefficients until the least error, i.e. 0,00012446 is obtained. Voice samples originated from the internal database system are 83% successfully recognized by this system. However; samples extracted from the human voice signals of different persons — different sex from the person whose voice signals are recorded in the database system, and from various ages — are only 78,8% successfully recognized by the system. The output of the speech recognition system is coded into the ASCII Codes and converted into the PWM signal to control the speed of a DC motor.","PeriodicalId":225743,"journal":{"name":"2017 15th International Conference on Quality in Research (QiR) : International Symposium on Electrical and Computer Engineering","volume":"193 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A DC motor speed control using the LPC-ANFIS speech recognition system\",\"authors\":\"M. Akil, I. Nurtanio, R. Sadjad\",\"doi\":\"10.1109/QIR.2017.8168484\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The aim of this research is to design an implementation of the speech recognition system to control the speed of a DC motor. The Linear Predictive Coding (LPC) method is used in the speed recognition system, tuned by the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) method. There are 5 (five) samples of voice signals in Bahasa Indonesia recognized by this system, i.e.: “Nyala”, “Lambat”, “Sedang”, “Cepat” and “Mati”. Every voice signal is repeated 5 (five) times until as many as 25 samples are recorded. Their voice characteristics are extracted using the LPC method represented by the LPC coefficients stored in a database system. The ANFIS method is implemented in 50 iterations to tune and to train the LPC coefficients until the least error, i.e. 0,00012446 is obtained. Voice samples originated from the internal database system are 83% successfully recognized by this system. However; samples extracted from the human voice signals of different persons — different sex from the person whose voice signals are recorded in the database system, and from various ages — are only 78,8% successfully recognized by the system. The output of the speech recognition system is coded into the ASCII Codes and converted into the PWM signal to control the speed of a DC motor.\",\"PeriodicalId\":225743,\"journal\":{\"name\":\"2017 15th International Conference on Quality in Research (QiR) : International Symposium on Electrical and Computer Engineering\",\"volume\":\"193 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 15th International Conference on Quality in Research (QiR) : International Symposium on Electrical and Computer Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/QIR.2017.8168484\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 15th International Conference on Quality in Research (QiR) : International Symposium on Electrical and Computer Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/QIR.2017.8168484","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A DC motor speed control using the LPC-ANFIS speech recognition system
The aim of this research is to design an implementation of the speech recognition system to control the speed of a DC motor. The Linear Predictive Coding (LPC) method is used in the speed recognition system, tuned by the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) method. There are 5 (five) samples of voice signals in Bahasa Indonesia recognized by this system, i.e.: “Nyala”, “Lambat”, “Sedang”, “Cepat” and “Mati”. Every voice signal is repeated 5 (five) times until as many as 25 samples are recorded. Their voice characteristics are extracted using the LPC method represented by the LPC coefficients stored in a database system. The ANFIS method is implemented in 50 iterations to tune and to train the LPC coefficients until the least error, i.e. 0,00012446 is obtained. Voice samples originated from the internal database system are 83% successfully recognized by this system. However; samples extracted from the human voice signals of different persons — different sex from the person whose voice signals are recorded in the database system, and from various ages — are only 78,8% successfully recognized by the system. The output of the speech recognition system is coded into the ASCII Codes and converted into the PWM signal to control the speed of a DC motor.
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