Characteristics-based effective applause detection for meeting speech
Characteristics-based effective applause detection for meeting speech
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DOI：
10.1016/j.sigpro.2009.03.001
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发表时间：
2009-08
期刊：
影响因子：
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通讯作者：
Yanxiong Li;Qianhua He;S. Kwong;Tao Li;Jichen Yang
中科院分区：
文献类型：
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作者：
Yanxiong Li;Qianhua He;S. Kwong;Tao Li;Jichen Yang
掌声在多人会议发言中经常出现。事实上，检测掌声对于满足语音识别、语义推理、亮点提取等功能非常重要。在本文中，我们将首先研究掌声和语音之间的特征差异，例如持续时间、音高、频谱图和出现位置。然后，基于这些特征提出了一种有效的算法来检测会议语音流中的掌声。该算法首先利用语音活动检测来提取非静音信号段。然后，基于掌声和语音之间的特征差异，从非静音信号片段中检测掌声片段，而不使用任何复杂的统计模型，例如隐马尔可夫模型。该算法能够准确地确定会议语音流中掌声的边界，并且计算效率高。此外，它还可以从混合片段中提取掌声子片段。实验评估表明，该算法在会议发言掌声检测中取得了满意的结果。准确率、召回率和 F1 测量值分别为 94.34%、98.04% 和 96.15%。与相同实验条件下的传统算法相比，F1-measure提高了3.62%，计算时间节省了约35.78%。
Applause frequently occurs in multi-participants meeting speech. In fact, detecting applause is quite important for meeting speech recognition, semantic inference, highlight extraction, etc. In this paper, we will first study the characteristic differences between applause and speech, such as duration, pitch, spectrogram and occurrence locations. Then, an effective algorithm based on these characteristics is proposed for detecting applause in meeting speech stream. In the algorithm, the non-silence signal segments are first extracted by using voice activity detection. Afterward, applause segments are detected from the non-silence signal segments based on the characteristic differences between applause and speech without using any complex statistical models, such as hidden Markov models. The proposed algorithm can accurately determine the boundaries of applause in meeting speech stream, and is also computationally efficient. In addition, it can extract applause sub-segments from the mixed segments. Experimental evaluations show that the proposed algorithm can achieve satisfactory results in detecting applause of the meeting speech. Precision rate, recall rate, and F1-measure are 94.34%, 98.04%, and 96.15%, respectively. When compared with the traditional algorithm under the same experimental conditions, 3.62% improvement in F1-measure is achieved, and about 35.78% of computational time is saved.