Automatic Recognition of Spontaneous Speech for Access to Multilingual Oral History Archives (2004)
| Venue: | IEEE Transactions on Speech and Audio Processing |
| Citations: | 20 - 6 self |
BibTeX
@ARTICLE{Byrne04automaticrecognition,
author = {William Byrne and David Doermann and Martin Franz and Senior Member and Samuel Gustman and Dagobert Soergel and Todd Ward and Wei-jing Zhu},
title = {Automatic Recognition of Spontaneous Speech for Access to Multilingual Oral History Archives},
journal = {IEEE Transactions on Speech and Audio Processing},
year = {2004},
pages = {420--435}
}
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Abstract
Abstract—Much is known about the design of automated systems to search broadcast news, but it has only recently become possible to apply similar techniques to large collections of spontaneous speech. This paper presents initial results from experiments with speech recognition, topic segmentation, topic categorization, and named entity detection using a large collection of recorded oral histories. The work leverages a massive manual annotation effort on 10 000 h of spontaneous speech to evaluate the degree to which automatic speech recognition (ASR)-based segmentation and categorization techniques can be adapted to approximate decisions made by human annotators. ASR word error rates near 40 % were achieved for both English and Czech for heavily accented, emotional and elderly spontaneous speech based on 65–84 h of transcribed speech. Topical segmentation based on shifts in the recognized English vocabulary resulted in 80 % agreement with
