首页> 外文会议>International Conference on Embedded Software and Systems(ICESS 2007); 20070514-16; Daegu(KR) >Voice/Non-Voice Classification Using Reliable Fundamental Frequency Estimator for Voice Activated Powered Wheelchair Control
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Voice/Non-Voice Classification Using Reliable Fundamental Frequency Estimator for Voice Activated Powered Wheelchair Control

机译:使用可靠的基本频率估计器进行语音/电动轮椅控制的语音/非语音分类

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摘要

In this paper, we introduce a non-voice rejection method to perform Voice/Non-Voice (V/NV) classification using a fundamental frequency (F0) estimator called YIN. Although current speech recognition technology has achieved high performance, it is insufficient for some applications where high reliability is required, such as voice control of powered wheelchairs for disabled persons. The V/NV classification algorithm, which rejects non-voice input in Voice Activity Detection (VAD), is helpful for realizing a highly reliable system. The proposed V/NV classification adopts the ratio of a reliable F_0 contour to the whole input interval. To evaluate the performance of our proposed method, we used 1567 voice commands and 447 noises in powered wheelchair control in a real environment. These results indicate that the recall rate is 97% when the lowest threshold is selected for noise classification with 99% precision in VAD.
机译:在本文中,我们介绍了一种非语音拒绝方法,该方法使用称为YIN的基频(F0)估计器执行语音/非语音(V / NV)分类。尽管当前的语音识别技术已经实现了高性能,但对于要求高可靠性的某些应用(例如,残疾人电动轮椅的语音控制)来说,这还不够。 V / NV分类算法可拒绝语音活动检测(VAD)中的非语音输入,有助于实现高度可靠的系统。提议的V / NV分类采用可靠的F_0轮廓与整个输入间隔的比率。为了评估我们提出的方法的性能,我们在真实环境中在电动轮椅控制中使用了1567个语音命令和447个噪音。这些结果表明,在VAD中以99%的精度选择最低阈值进行噪声分类时,召回率为97%。

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