Frequency band analysis for stress detection using a teager energy operator based feature
作者： Michael Koenig , James Meyerhoff , John H. L. Hansen , George Saviolakis , Mandar A. Rahurkar
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摘要: Studies have shown that the performance of speech recognition algorithms severely degrade due to presence task and emotional induced stress in adverse conditions. This paper addresses problem detecting by analyzing nonlinear feature characteristics specific frequency bands. The framework previously derived Teager Energy Operator(TEO) based TEO-CB-AutoEnv is used. A new detection scheme proposed on weighted TEO features from critical bands frequencies. evaluated a military corpus collected Soldier Quarter (SOQ) paradigm. Heart rate blood pressure measurements confirm subjects were under stress. Using traditional with an HMM trained stressed classifier, we show error rates 22.5% 13% for neutral detection. With sub-band scheme, are reduced 4.7% 4.6% detection, relative reduction 79.1% 64.6% respectively. Finally discuss issues related generation anchor models speaker dependency.
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researchgate.net 参考文章(9)
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