评价自发性会话语音排序检索效果的单侧测量方法

Baolong Liu, Douglas W. Oard
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引用次数: 32

摘要

早期的语音检索实验主要集中在新闻广播中,可以获得足够的自动语音识别(ASR)精度。像报纸一样,新闻广播是人工选择和安排的一组故事。评估设计反映了这一点,使用已知的故事边界作为评估的基础。在ASR准确度方面取得的实质性进展现在使得为某些类型的自发会话语音建立搜索系统成为可能,但是目前的评估设计仍然依赖于已知的主题边界,这些主题边界不再与材料的性质很好地匹配。我们提出了一种新的语音检索方法,该方法基于对具有特定主题兴趣的用户希望重播开始的点进行手动注释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
One-sided measures for evaluating ranked retrieval effectiveness with spontaneous conversational speech
Early speech retrieval experiments focused on news broadcasts, for which adequate Automatic Speech Recognition (ASR) accuracy could be obtained. Like newspapers, news broadcasts are a manually selected and arranged set of stories. Evaluation designs reflected that, using known story boundaries as a basis for evaluation. Substantial advances in ASR accuracy now make it possible to build search systems for some types of spontaneous conversational speech, but present evaluation designs continue to rely on known topic boundaries that are no longer well matched to the nature of the materials. We propose a new class of measures for speech retrieval based on manual annotation of points at which a user with specific topical interests would wish replay to begin.
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