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首页> 外文期刊>Multimedia Tools and Applications >Hybridized estimations of support vector machine free parameters C and gamma using a fuzzy learning strategy for microphone array-based speaker recognition in a Kinect sensor-deployed environment
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Hybridized estimations of support vector machine free parameters C and gamma using a fuzzy learning strategy for microphone array-based speaker recognition in a Kinect sensor-deployed environment

机译：Kinect传感器部署环境中基于麦克风阵列的说话人识别的模糊学习策略的支持向量机自由参数C和γ的混合估计

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摘要

The support vector machine (SVM) is a popular classification model for speaker verification. However, although SVM is suitable for classifying speakers, the uncertain values of the free parameters C and gamma of the SVM model have been a challenging technique problem. An improper value set provided for the free parameter pair (C, gamma) can cause dissatisfactory performance in the recognition accuracy of speaker verification. Moreover, the sound source localization information of the collected acoustic data has a large effect on the recognition performance of SVM speaker verification. In response, this study developed a sound source localization-driven fuzzy scheme to help determine the optimal value set of (C, gamma) for the establishment of an SVM model. Specifically, this scheme adopts the estimated information of time difference of arrival (TDOA) derived from the Kinect microphone array (containing both the angle and distance information of the acoustic data of the speaker), to optimally calculate the value set of the SVM free parameters C and gamma. It was demonstrated that speaker verification using the SVM with a properly estimated parameter pair (C, gamma) is more accurate than that with only an arbitrarily given value set for the parameter pair (C, gamma) on recognition rate.
机译：支持向量机（SVM）是用于说话者验证的流行分类模型。但是，尽管SVM适用于对说话者进行分类，但是SVM模型的自由参数C和伽马的不确定值一直是一个具有挑战性的技术问题。为自由参数对（C，γ）提供的值设置不正确会导致说话人验证的识别精度表现不理想。此外，所收集的声学数据的声源定位信息对SVM说话者验证的识别性能具有很大的影响。作为回应，本研究开发了声源定位驱动的模糊方案，以帮助确定用于建立SVM模型的（C，gamma）最佳值集。具体而言，该方案采用从Kinect麦克风阵列派生的估计到达时间差（TDOA）信息（包含扬声器声学数据的角度和距离信息），以最佳方式计算SVM免参数的值集C和伽玛。已证明，使用带有适当估计的参数对（C，gamma）的SVM进行的说话人验证比仅对参数对（C，gamma）的识别率设置任意给定值的情况更准确。

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