{"title":"视听广播转录系统的原型","authors":"J. Chaloupka","doi":"10.1109/TSP.2019.8769103","DOIUrl":null,"url":null,"abstract":"This paper focuses on the use of methods and algorithms from the area of speech processing and recognition and from the area of machine vision for designing of system for automatic audio-visual broadcast transcription. The resulting audio-visual system has been designed and created mainly for transcription of huge video databases with TV recordings in this work. The visual signal processing and recognition is usually several times computationally more demanding than audio signal processing and recognition. Therefore, all applied machine vision methods and algorithms were considered with respect to low computing time as well as the highest possible recognition rate. Our proposed broadcast transcription system was extended by several modules for visual signal segmentation, for TV channel identification, for face detection and identification and for Optical Character Recognition (OCR).","PeriodicalId":399087,"journal":{"name":"2019 42nd International Conference on Telecommunications and Signal Processing (TSP)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A prototype of Audio-Visual Broadcast Transcription System\",\"authors\":\"J. Chaloupka\",\"doi\":\"10.1109/TSP.2019.8769103\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper focuses on the use of methods and algorithms from the area of speech processing and recognition and from the area of machine vision for designing of system for automatic audio-visual broadcast transcription. The resulting audio-visual system has been designed and created mainly for transcription of huge video databases with TV recordings in this work. The visual signal processing and recognition is usually several times computationally more demanding than audio signal processing and recognition. Therefore, all applied machine vision methods and algorithms were considered with respect to low computing time as well as the highest possible recognition rate. Our proposed broadcast transcription system was extended by several modules for visual signal segmentation, for TV channel identification, for face detection and identification and for Optical Character Recognition (OCR).\",\"PeriodicalId\":399087,\"journal\":{\"name\":\"2019 42nd International Conference on Telecommunications and Signal Processing (TSP)\",\"volume\":\"138 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 42nd International Conference on Telecommunications and Signal Processing (TSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TSP.2019.8769103\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 42nd International Conference on Telecommunications and Signal Processing (TSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TSP.2019.8769103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A prototype of Audio-Visual Broadcast Transcription System
This paper focuses on the use of methods and algorithms from the area of speech processing and recognition and from the area of machine vision for designing of system for automatic audio-visual broadcast transcription. The resulting audio-visual system has been designed and created mainly for transcription of huge video databases with TV recordings in this work. The visual signal processing and recognition is usually several times computationally more demanding than audio signal processing and recognition. Therefore, all applied machine vision methods and algorithms were considered with respect to low computing time as well as the highest possible recognition rate. Our proposed broadcast transcription system was extended by several modules for visual signal segmentation, for TV channel identification, for face detection and identification and for Optical Character Recognition (OCR).