Automated transcription and topic segmentation of large spoken archives
Automated transcription and topic segmentation of large spoken archives
复制标题
大型语音档案的自动转录和主题分割
DOI：
10.21437/eurospeech.2003-331
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发表时间：
2003
期刊：
影响因子：
7.5
通讯作者：
M. Picheny
中科院分区：
文献类型：
--
作者：
M. Franz;B. Ramabhadran;T. Ward;M. Picheny
Digital archives have emerged as the pre-eminent method for capturing the human experience. Before such archives can be used efficiently, their contents must be described. The scale of such archives along with the associated content mark up cost make it impractical to provide access via purely manual means, but automatic technologies for search in spoken materials still have relatively limited capabilities. The NSF-funded MALACH project will use the world’s largest digital archive of video oral histories, collected by the Survivors of the Shoah Visual History Foundation (VHF) to make a quantum leap in the ability to access such archives by advancing the state-of-the-art in Automated Speech Recognition (ASR), Natural Language Processing (NLP) and related technologies [1, 2]. This corpus consists of over 115,000 hours of unconstrained, natural speech from 52,000 speakers in 32 different languages, filled with disfluencies, heavy accents, age-related coarticulations, and un-cued speaker and language switching. Thispaper discusses some of theASR and NLPtools and technologies that we have been building for the English speech in the MALACH corpus. We also discuss this new test bed while emphasizing the unique characteristics of this corpus.
