{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:47:45Z","timestamp":1776811665675,"version":"3.51.2"},"reference-count":20,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2023,5,30]]},"abstract":"<jats:p>With the rapid development of artificial intelligence and the continuous improvement of machine learning technology, speech recognition technology is also developing rapidly and the recognition accuracy is improving to meet the higher requirements of people for smart home devices, and combining smart home with voice recognition technology is an inevitable trend for future development. This study aims to propose a speech fuzzy enhancement algorithm based on neural network for smart home interactive speech recognition technology, so the study proposes a combination of fuzzy neural network algorithm (FNN) and stacked self-encoder (SAE) to form SAE-FNN algorithm, which has better non-linear characteristics and can better achieve feature learning, thus improving the performance of the whole system. The results show that with the SAE-FNN algorithm, the maximum relative error absolute value, average relative error and root mean square error are 0.355, 0.063 and 0.978, which are significantly higher than the other two individual algorithms, and the noise of the sound signal has little effect on the SAE-FNN algorithm. Therefore, it can be seen that the proposed SAE-FNN algorithm has excellent noise immunity performance. In summary, it can be seen that this neural network-based speech fuzzy enhancement algorithm for smart home interaction is extremely feasible.<\/jats:p>","DOI":"10.3233\/jcm-226702","type":"journal-article","created":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T11:46:02Z","timestamp":1675165562000},"page":"1225-1236","source":"Crossref","is-referenced-by-count":2,"title":["Neural network-based speech fuzzy enhancement algorithm for smart home interaction"],"prefix":"10.66113","volume":"23","author":[{"given":"Yongjian","family":"Dong","sequence":"first","affiliation":[{"name":"Academic Affairs office, Changzhou Vocational Institute of Mechatronic Technology, Changzhou, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinrong","family":"Ye","sequence":"additional","affiliation":[{"name":"Changzhou National High Tech Zone, Economic Development Bureau, Development and Reform Division, Changzhou, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","reference":[{"key":"10.3233\/JCM-226702_ref1","first-page":"6610461","article-title":"Proposing a recognition system of gestures using MobilenetV2 combining single shot detector network for smart-home applications","volume":"2021","author":"Huu","year":"2021","journal-title":"J Electr Comput Eng."},{"key":"10.3233\/JCM-226702_ref2","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.zefq.2021.05.004","article-title":"Smart sensorik in der schwangerschaft: narratives review \u00fcber die verlagerung der schwangerschaftsvorsorge in den smart home bereich","volume":"164","author":"Bossung","year":"2021","journal-title":"Zeitschrift f\u00fcr Evidenz, Fortbildung und Qualit\u00e4t im Gesundheitswesen."},{"issue":"3","key":"10.3233\/JCM-226702_ref3","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1109\/MNET.011.2000514","article-title":"A machine learning approach for blockchain-based smart home networks security","volume":"35","author":"Khan","year":"2021","journal-title":"IEEE Network."},{"key":"10.3233\/JCM-226702_ref4","doi-asserted-by":"crossref","first-page":"16807","DOI":"10.1109\/ACCESS.2021.3051937","article-title":"A smart home architecture for smart energy consumption in a residence with multiple users","volume":"9","author":"Andrade","year":"2021","journal-title":"IEEE Access."},{"key":"10.3233\/JCM-226702_ref5","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1016\/j.future.2019.09.020","article-title":"Exploring the cognitive process for service task in smart home: A robot service mechanism","volume":"102","author":"Zhang","year":"2020","journal-title":"Future Gener Comput Syst."},{"issue":"2","key":"10.3233\/JCM-226702_ref6","doi-asserted-by":"crossref","first-page":"17101","DOI":"10.1016\/j.ifacol.2020.12.1649","article-title":"Multiple kernel based transfer learning for the few-shot recognition task in smart home scene","volume":"53","author":"Chang","year":"2020","journal-title":"IFAC-PapersOnLine."},{"key":"10.3233\/JCM-226702_ref7","doi-asserted-by":"crossref","first-page":"184151","DOI":"10.1109\/ACCESS.2020.3023665","article-title":"A fast and optimal smart home energy management system: State-space approximate dynamic programming","volume":"8","author":"Zhao","year":"2020","journal-title":"IEEE Access."},{"key":"10.3233\/JCM-226702_ref8","doi-asserted-by":"crossref","first-page":"92907","DOI":"10.1109\/ACCESS.2020.2993008","article-title":"Automatic vehicle license plate recognition using optimal k-means with convolutional neural network for intelligent transportation systems","volume":"8","author":"Pustokhina","year":"2020","journal-title":"IEEE Access."},{"key":"10.3233\/JCM-226702_ref9","doi-asserted-by":"crossref","unstructured":"Kulkarni A, Kulkarni N. 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