{"title":"基于人工神经网络的嵌入式语音识别系统的实现","authors":"Pranjali P. Patange, J. Alex","doi":"10.1109/ICNETS2.2017.8067968","DOIUrl":null,"url":null,"abstract":"Speech recognition systems are ubiquitous and find its application in automated voice control, voice dialling and automated directory assistance. This paper aims at implementing a neural network based isolated spoken word recognition system on an embedded board — Raspberry Pi using open source software called octave. Mel-Frequency Cepstral Coefficient (MFCC) features are extracted from speech signal and given as input to the neural network. The Feed Forward Multi-Layer Perceptron Neural Network trained with back propagation rule is implemented using Octave in Raspberry Pi. TIDIGITS corpus is used for the experiment. Speaker dependent speech recognition results in 100% accuracy but the speaker independent recognition system shows less accuracy.","PeriodicalId":413865,"journal":{"name":"2017 International Conference on Nextgen Electronic Technologies: Silicon to Software (ICNETS2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Implementation of ANN based speech recognition system on an embedded board\",\"authors\":\"Pranjali P. Patange, J. Alex\",\"doi\":\"10.1109/ICNETS2.2017.8067968\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Speech recognition systems are ubiquitous and find its application in automated voice control, voice dialling and automated directory assistance. This paper aims at implementing a neural network based isolated spoken word recognition system on an embedded board — Raspberry Pi using open source software called octave. Mel-Frequency Cepstral Coefficient (MFCC) features are extracted from speech signal and given as input to the neural network. The Feed Forward Multi-Layer Perceptron Neural Network trained with back propagation rule is implemented using Octave in Raspberry Pi. TIDIGITS corpus is used for the experiment. Speaker dependent speech recognition results in 100% accuracy but the speaker independent recognition system shows less accuracy.\",\"PeriodicalId\":413865,\"journal\":{\"name\":\"2017 International Conference on Nextgen Electronic Technologies: Silicon to Software (ICNETS2)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Nextgen Electronic Technologies: Silicon to Software (ICNETS2)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNETS2.2017.8067968\",\"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 International Conference on Nextgen Electronic Technologies: Silicon to Software (ICNETS2)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNETS2.2017.8067968","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Implementation of ANN based speech recognition system on an embedded board
Speech recognition systems are ubiquitous and find its application in automated voice control, voice dialling and automated directory assistance. This paper aims at implementing a neural network based isolated spoken word recognition system on an embedded board — Raspberry Pi using open source software called octave. Mel-Frequency Cepstral Coefficient (MFCC) features are extracted from speech signal and given as input to the neural network. The Feed Forward Multi-Layer Perceptron Neural Network trained with back propagation rule is implemented using Octave in Raspberry Pi. TIDIGITS corpus is used for the experiment. Speaker dependent speech recognition results in 100% accuracy but the speaker independent recognition system shows less accuracy.
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