Nirupam Shome, Anisha Sarkar, Arit Ghosh, R. Laskar, Richik Kashyap
{"title":"通过深度学习技术识别说话人:全面回顾和研究挑战","authors":"Nirupam Shome, Anisha Sarkar, Arit Ghosh, R. Laskar, Richik Kashyap","doi":"10.3311/ppee.20971","DOIUrl":null,"url":null,"abstract":"Deep learning has now become an integral part of today's world and advancement in the field of deep learning has gained a huge development. Due to the extensive use and fast growth of deep learning, it has captured the attention of researchers in the field of speaker recognition. A detailed investigation regarding the process becomes essential and helpful to the researchers for designing robust applications in the field of speaker recognition, both in speaker verification and identification. This paper reviews the field of speaker recognition taking into consideration of deep learning advancement in the present era that boosts up this technology. The paper continues with a systematic review by firstly giving a basic idea of deep learning and its architecture with its field of application, then entering into the high-lighted portion of our paper i.e., speaker recognition which is one of the important applications of deep learning. Here we have mentioned its types, different processing techniques, challenges that come across in this technology, performance evaluation criteria, deep learning implementation frameworks, and lastly various databases used in the field of speaker identification (SI) and Speaker Verification (SV).","PeriodicalId":37664,"journal":{"name":"Periodica polytechnica Electrical engineering and computer science","volume":"31 1","pages":"300-336"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Speaker Recognition through Deep Learning Techniques: A Comprehensive Review and Research Challenges\",\"authors\":\"Nirupam Shome, Anisha Sarkar, Arit Ghosh, R. Laskar, Richik Kashyap\",\"doi\":\"10.3311/ppee.20971\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Deep learning has now become an integral part of today's world and advancement in the field of deep learning has gained a huge development. Due to the extensive use and fast growth of deep learning, it has captured the attention of researchers in the field of speaker recognition. A detailed investigation regarding the process becomes essential and helpful to the researchers for designing robust applications in the field of speaker recognition, both in speaker verification and identification. This paper reviews the field of speaker recognition taking into consideration of deep learning advancement in the present era that boosts up this technology. The paper continues with a systematic review by firstly giving a basic idea of deep learning and its architecture with its field of application, then entering into the high-lighted portion of our paper i.e., speaker recognition which is one of the important applications of deep learning. Here we have mentioned its types, different processing techniques, challenges that come across in this technology, performance evaluation criteria, deep learning implementation frameworks, and lastly various databases used in the field of speaker identification (SI) and Speaker Verification (SV).\",\"PeriodicalId\":37664,\"journal\":{\"name\":\"Periodica polytechnica Electrical engineering and computer science\",\"volume\":\"31 1\",\"pages\":\"300-336\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Periodica polytechnica Electrical engineering and computer science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3311/ppee.20971\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Periodica polytechnica Electrical engineering and computer science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3311/ppee.20971","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}
Speaker Recognition through Deep Learning Techniques: A Comprehensive Review and Research Challenges
Deep learning has now become an integral part of today's world and advancement in the field of deep learning has gained a huge development. Due to the extensive use and fast growth of deep learning, it has captured the attention of researchers in the field of speaker recognition. A detailed investigation regarding the process becomes essential and helpful to the researchers for designing robust applications in the field of speaker recognition, both in speaker verification and identification. This paper reviews the field of speaker recognition taking into consideration of deep learning advancement in the present era that boosts up this technology. The paper continues with a systematic review by firstly giving a basic idea of deep learning and its architecture with its field of application, then entering into the high-lighted portion of our paper i.e., speaker recognition which is one of the important applications of deep learning. Here we have mentioned its types, different processing techniques, challenges that come across in this technology, performance evaluation criteria, deep learning implementation frameworks, and lastly various databases used in the field of speaker identification (SI) and Speaker Verification (SV).
期刊介绍:
The main scope of the journal is to publish original research articles in the wide field of electrical engineering and informatics fitting into one of the following five Sections of the Journal: (i) Communication systems, networks and technology, (ii) Computer science and information theory, (iii) Control, signal processing and signal analysis, medical applications, (iv) Components, Microelectronics and Material Sciences, (v) Power engineering and mechatronics, (vi) Mobile Software, Internet of Things and Wearable Devices, (vii) Solid-state lighting and (viii) Vehicular Technology (land, airborne, and maritime mobile services; automotive, radar systems; antennas and radio wave propagation).