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首页> 外文会议>Annual conference of the International Speech Communication Association;INTERSPEECH 2011 >Fusing Multiple Confidence Measures for Chinese Spoken Term Detection
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Fusing Multiple Confidence Measures for Chinese Spoken Term Detection

机译：融合多重置信度的汉语口语检测

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摘要

In spoken term detection (STD) task, the confidence measure is used to assess the reliability of detected terms. The widely used confidence measure in STD is based on the normalized lattice posterior probability. In this paper, however, several distinct confidence estimation methods are investigated to improve the baseline lattice confidence: the acoustic and duration confidences are estimated by hybrid Hidden Markov Model/Artificial Neural Network (HMM/ANN) and phonetic duration model respectively. These two confidences plus lattice confidence are linearly interpolated to produce a more reliable confidence measure. The experimental results show the feasibility and effectiveness of our combination approach. The proposed method substantially improves the STD performance, for a 4.8%-ll.l% relative equal error rate (EER) reduction on three evaluation sets compared with the baseline lattice confidence.
机译：在语音术语检测（STD）任务中，置信度量度用于评估检测到的术语的可靠性。 STD中广泛使用的置信度度量基于归一化的格点后验概率。然而，在本文中，研究了几种不同的置信度估计方法以提高基线格置信度：声学和持续时间置信度分别通过混合隐马尔可夫模型/人工神经网络（HMM / ANN）和语音持续时间模型进行估计。对这两个置信度加上晶格置信度进行线性插值以产生更可靠的置信度度量。实验结果表明了该组合方法的可行性和有效性。与基线晶格置信度相比，在三个评估集上相对平均错误率（EER）降低了4.8％-1.1％，因此，所提出的方法大大提高了STD性能。

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