We propose a robust and fast d巴reverberation technique for real-time speech recognition application. First， we effectively identifシthe late reflection components of the room impulse response. We use this information together with the con cept of Spectral Subtraction (SS) to remove the late refl ection components of the reverberant signal. In the absence of the c]ean speech in actual scenario， approximation is carried out in estimating the late reflection where the estimation e汀or is corrected through multi-band SS. The multi-band coefficients are optimized during offline training加d used in the actual online dereverberation. The proposed method performs bet ter and faster th叩the relevant approach using Multi-LPC and reverberant matched model. Moreover the proposed method is robust to sp巴水er and microphone locations.

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A robust dereverberation technique for real-time hands free speech recognition application is proposed. Real-time implementation is mad巴 possible by avoiding time-consuming blind estimation. Instead， we us巴 the impulse response by ef fectively identifying the late reftection components of it. Us ing this information， togeth巴r with the concept of 5pectral 5ubtraction (55)， we were able to remove the effects of the late reftection of the reverb巴rant signal. After dereverberation，

This paper presents a study on multi-modal human emotional state detection while riding a powered wheelchair (PMV; Personal Mobility Vehicle) in an indoor labyrinth-like environment. The study reports findings on the habituation of human stress response during self-driving. In addition, the effects of “loss of controllability”, change in the role of the driver to a passenger, are investigated via an autonomous driving modality. The multi-modal emotional state detector sensing framework consists of four sensing devices: electroencephalograph (EEG), heart inter-beat interval (IBI), galvanic skin response (GSR) and stressor level lever (in the case of autonomous riding). Physiological emotional state measurement characteristics are organized by time-scale, in terms of capturing slower changes (long-term) and quicker changes from moment-to-moment. Experimental results with fifteen participants regarding subjective emotional state reports and commercial software measurements validated the proposed emotional state detector. Short-term GSR and heart signal characterizations captured moment-to-moment emotional state during autonomous riding (Spearman correlation; ρ = 0.6, p < 0.001). Short-term GSR and EEG characterizations reliably captured moment-to-moment emotional state during self-driving (Classification accuracy; 69.7). Finally, long-term GSR and heart characterizations were confirmed to reliably capture slow changes during autonomous riding and also of emotional state during participant resting state. The purpose of this study and the exploration of various algorithms and sensors in a structured framework is to provide a comprehensive background for multi-modal emotional state prediction experiments and/or applications. Additional discussion regarding the feasibility and utility of the possibilities of these concepts are given.

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