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AI-Assisted IPTV Broadcasting System for Real-Time Automatic VOD Subtitle Generation

첫 페이지 보기
  • 발행기관
    국제인공지능학회(구 한국인터넷방송통신학회) 바로가기
  • 간행물
    International Journal of Internet, Broadcasting and Communication 바로가기
  • 통권
    Vol.17 No.4 (2025.11)바로가기
  • 페이지
    pp.492-501
  • 저자
    MyeungHoon Kim, JaRyeong Koo, Velda Vania, WonJun Yoon, MinHong Lee, Daewon Song
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A486508

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원문정보

초록

영어
In this paper, we present a novel AI-assisted IPTV broadcasting system (AIBS) for real-time automatic VOD subtitle generation. The proposed system generates subtitles using Digital TV-Closed Caption (DTV-CC) data and decides the display time of VOD subtitles via utilizing the latest AI technology. With this approach, the problems of subtitle accuracy and display time errors of existing STT systems can be resolved. First, we introduce a method for obtaining subtitle information through DTV-CC in the existing IPTV broadcasting system. Then, the align function of the Stable-TS is adopted to obtain the display time of the subtitles and, for a more accurate time, various noises other than voices in the input audio signal are filtered with deep learningbased background noise removal technology as pre-processing. Finally, if the subtitle display time provided by Stable-TS is abnormal, the display time information is corrected using an NLP-based post-processing technology that utilizes the speaker separation information and relative time information of DTV-CC. Experimental results shows that the average accuracy, which was 87% when using Stable-TS alone, improved by 7% to an average of 94% after applying AIBS on testing the accuracy of subtitle synchronization for approximately 3,000 sentences. In addition, by applying AIBS to the existing IPTV broadcasting system that generates quick-VOD based on linear channels, the problem of manually generating VOD subtitles that took 4 hours was reduced to less than 10 minutes, improving customer satisfaction.

목차

Abstract
1. Introduction
2. Related Works
2.1 Advancements and Challenges in STT Technology
2.2 Whisper Model and Latest Extention Technologies
3. The Proposed System
3.1 Integration with VoD Quick Delivery System and DTV-CC extraction
3.2 Audio Processing
3.3 Post-Processing with DTV-CC
4. The Experimental Results
5. Conclusions
References

키워드

IPTV VOD DTV-CC AI Model Subtitle Stable-TS Deep-Learning

저자

  • MyeungHoon Kim [ Senior Research Engineer, Agent Engineering 2 Team, Technology Development Group, LGUplus ]
  • JaRyeong Koo [ Chief Research Engineer, Home Media Agent Development Team, Technology Development Group, LGUplus ]
  • Velda Vania [ AI Researcher, AI R&D Team, STRA ]
  • WonJun Yoon [ Chief Research Engineer, Home Media Agent Development Team, Technology Development Group, LGUplus ] Corresponding Author
  • MinHong Lee [ General Manager, Lab Leader, Home Service Development Lab., LGUplus ]
  • Daewon Song [ Vice President, Group Leader, Technology Development Group, LGUplus ]

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    국제인공지능학회(구 한국인터넷방송통신학회) [The International Association for Artificial Intelligence]
  • 설립연도
    2000
  • 분야
    공학>전자/정보통신공학
  • 소개
    인터넷방송, 인터넷 TV , 방송 통신 네트워크 및 관련 분야에 대한 국내는 물론 국제적인 학술, 기술의 진흥발전에 공헌하고 지식 정보화 사회에 기여하고자 한다.

간행물

  • 간행물명
    International Journal of Internet, Broadcasting and Communication
  • 간기
    계간
  • pISSN
    2288-4920
  • eISSN
    2288-4939
  • 수록기간
    2009~2025
  • 십진분류
    KDC 326 DDC 380

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