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飞行驾驶员的应答方式识别

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飞行驾驶员的应答方式识别
Speaking Style Recognition of Pilots in Flight
投稿时间:2015-11-02
DOI:10.15918/j.tbit1001-0645.2017.07.016
中文关键词:副语言学语音信号说话方式识别
English Keywords:computation paralinguisticspeech signalspeaking style recognition
基金项目:国家自然科学基金资助项目(61473041,11590772,11590770)
作者单位
谢湘北京理工大学 信息与电子学院, 北京 100081
唐刚北京理工大学 信息与电子学院, 北京 100081
肖泽苹北京理工大学 信息与电子学院, 北京 100081
李通北京理工大学 信息与电子学院, 北京 100081
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中文摘要:
飞机驾驶员在飞行过程中有各种各样的说话方式,如带情感的对话、快速与慢速、大声与小声等.并且在飞行的状态下还需要承受物理与心理压力,对话语音还会产生变异,如果不做任何处理,直接用于传统的说话人识别系统以及语音识别系统来处理,性能会比较差.因此,本文对识别飞行员的说话方式(style)这一副语言信息进行了研究,以辅助后续的语音识别系统以及说话人识别系统.实验数据库包含了6 925个样本,实验中提取了384维声学特征,比较了支持向量机SVM不同核函数的分类能力.实验表明,采用高斯径向基函数的SVM具有最好的性能,平均准确率达到91.62%.
English Summary:
The pilots have various speaking styles in flight, such as emotional dialogue, speaking fast and slowly, speaking loudly and quietly, etc. Moreover, the physical and mental pressure of the pilots in flight can cause dialogue speech aberrance. If the speech was processed directly by the traditional speaker recognition system or a speech recognition system without any processing function, the speech performance will be poor. Therefore, the recognition of the pilots' speaking styles, a kind of paralinguistic information, was investigated to assist the subsequent speech recognition system and speaker recognition system. In the study, 6 925 samples were collected in the experiment database, 384-dimension acoustic features were extracted, and compared the classification ability of SVMs with different Kernel functions. The experiment results indicated that the SVM with Gauss radial basis Kernel function shows the best performance and its accuracy can reach 91.62%.
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