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首页> 外文会议>Conference on the North American Chapter of the Association for Computational Linguistics: Human Language Technologies >Disfluency Detection with a Semi-Markov Model and Prosodic Features
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Disfluency Detection with a Semi-Markov Model and Prosodic Features

机译：半马尔可夫模型和韵律特征的流淌检测

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摘要

We present a discriminative model for detecting disfluencies in spoken language transcripts. Structurally, our model is a semi-Markov conditional random field with features targeting characteristics unique to speech repairs. This gives a significant performance improvement over standard chain-structured CRFs that have been employed in past work. We then incorporate prosodic features over silences and relative word duration into our semi-CRF model, resulting in further performance gains; moreover, these features are not easily replaced by discrete prosodic indicators such as ToBI breaks. Our final system, the semi-CRF with prosodic information, achieves an F-score of 85.4, which is 1.3 F_1 better than the best prior reported F-score on this dataset.
机译：我们提出了一种判别模型，用于检测口语成绩单中的歧义。从结构上讲，我们的模型是一个半马尔可夫条件随机场，具有针对语音修复特有特征的特征。与过去工作中采用的标准链结构C​​RF相比，这可以显着提高性能。然后，我们将沉默和相对词持续时间的韵律特征纳入我们的半CRF模型中，从而进一步提高性能;此外，这些功能不易被离散的韵律指示符（如ToBI中断）取代。我们的最终系统是带有韵律信息的半CRF，其F得分达到85.4，比该数据集上先前报告的最佳F得分高1.3 F_1。

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