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廣播新聞?wù)Z料識別中的自動(dòng)分段和分類算法

呂萍 顏永紅

呂萍, 顏永紅. 廣播新聞?wù)Z料識別中的自動(dòng)分段和分類算法[J]. 電子與信息學(xué)報(bào), 2006, 28(12): 2292-2295.
引用本文: 呂萍, 顏永紅. 廣播新聞?wù)Z料識別中的自動(dòng)分段和分類算法[J]. 電子與信息學(xué)報(bào), 2006, 28(12): 2292-2295.
Lü Ping, Yan Yong-hong. Audio Segmentation and Classification in a Broadcast News Task[J]. Journal of Electronics & Information Technology, 2006, 28(12): 2292-2295.
Citation: Lü Ping, Yan Yong-hong. Audio Segmentation and Classification in a Broadcast News Task[J]. Journal of Electronics & Information Technology, 2006, 28(12): 2292-2295.

廣播新聞?wù)Z料識別中的自動(dòng)分段和分類算法

Audio Segmentation and Classification in a Broadcast News Task

  • 摘要: 該介紹了中文廣播新聞?wù)Z料識別任務(wù)中的自動(dòng)分段和自動(dòng)分類算法。提出了3階段自動(dòng)分段系統(tǒng)。該方法通過粗分段、精細(xì)分段和平滑3個(gè)階段,將音頻流分割為易于識別的音頻段。在精細(xì)分段階段,文中提出兩種算法:動(dòng)態(tài)噪聲跟蹤分段算法和基于單音素解碼的分段算法。仿效說話人鑒別中的方法,文中提出了基于混合高斯模型的分類算法。該算法較好地解決了音頻段的多類判決問題。在新聞聯(lián)播測試數(shù)據(jù)中的實(shí)驗(yàn)結(jié)果表明,該文提出的自動(dòng)分段和分類算法性能與手工分段分類性能幾乎相當(dāng)。
  • David Graff. An overview of broadcast news corpora[J].Speech Communication.2002, 37(1):15-26[2]Pallett D S. A look a NISTs benchmark ASR tests: Past, present,and future. IEEE 2003 Automatic Speech Recognition and Understanding workshop, U S. Virgin Islands, 30 Nov.-3 Dec., 2003: 483 - 488.[3]Wayne C. Mutilingual topic detection and tracking: Successful research enabled by corpora and evaluation. Language Resources and Evaluation Conference (LREC), Athens, Greece, 31 May-2 June, 2000: 1487-1494.[4]David S. Automatic transcription of broadcast news data[J].Speech Communication.2002, 37(1):1-2[5]Robinson A J. Connectionist speech recognition of broadcast news[J].Speech Communication.2002, 37(1):27-45[6]Woodland P C. The development of the HTK broadcast news transcription system: An overview[J].Speech Communication.2002, 37(1):47-67[7]Hung Jeih-Weih. Automatic metric-based speech segmentation for broadcast news via principal component analysis. In International Conference on Spoken Language Processing (ICSLP) 2000, Beijing China, October 16-20, 2000, (4): 121-124.[8]Cheng Shi-sian. A sequential metric-based audio segmentation method via the Bayesian information criterion. EuroSpeech 2003, Geneva, Switzerland, Sep. 1-4, 2003: 945-948.[9]Lin L. Speech enhancement for nonstationary noise environment. Asia-Pacific Conference on Circuits and Systems, 2002, Singapore, Oct. 28-31, 2002, Vol(1): 177-180.[10]Yamamoto H. Parameter sharing and minimum classification error training of mixtures of factor analyzers for speaker identification. IEEE International Conference on Acoustics Speech and Signal Processing 2004, Montreal Canada, May 17-21, 2004, Vol(1): 17-21.[11]Legetter C J. Maximum likelihood linear regression for speaker adaptation of continuous density HMMs[J].Computer Speech and Language.1995, 9(2):171-186
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出版歷程
  • 收稿日期:  2005-04-06
  • 修回日期:  2005-09-20
  • 刊出日期:  2006-12-19

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