Using simple speech-based features to detect the state of a meeting and the roles of the meeting participants
Using simple speech-based features to detect the state of a meeting and the roles of the meeting participants
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DOI：
10.21437/interspeech.2004-241
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
Satanjeev Banerjee;Alexander I. Rudnicky
中科院分区：
文献类型：
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作者：
Satanjeev Banerjee;Alexander I. Rudnicky
我们介绍了一个简单的分类会议状态和参与者角色。我们的目标是自动检测会议的状态和每个会议参与者的角色，并与会议同时进行。我们训练了一个决策树分类器，它可以学习从简单的基于语音的特征中检测这些状态和角色，这些特征很容易自动计算。该分类器检测会议状态比随机分类器准确18%，检测参与者角色比多数分类器准确10%。结果表明，简单，易于计算的功能可以用于此目的。
We introduce a simple taxonomy of meeting states and participant roles. Our goal is to automatically detect the state of a meeting and the role of each meeting participant and to do so concurrent with a meeting. We trained a decision tree classifier that learns to detect these states and roles from simple speech–based features that are easy to compute automatically. This classifier detects meeting states 18% absolute more accurately than a random classifier, and detects participant roles 10% absolute more accurately than a majority classifier. The results imply that simple, easy to compute features can be used for this purpose.