We propose a method for accurately identifying people using temporal and spatial changes in local movements measured from video sequences of body sway. Existing methods identify people using gait features that mainly represent the large swinging of the limbs. The use of gait features introduces a problem in that the identification performance decreases when people stop walking and maintain an upright posture. To extract informative features, our method measures small swings of the body, referred to as body sway. We extract the power spectral density as a feature from local body sway movements by dividing the body into regions. To evaluate the identification performance using our method, we collected three original video datasets of body sway sequences. The first dataset contained a large number of participants in an upright posture. The second dataset included variation over the long term. The third dataset represented body sway in different postures. The results on the datasets confirmed that our method using local movements measured from body sway can extract informative features for identification.

This paper presents a generating method of LSP parameter sequences for speech synthesis by rule. In our method, neural networks are schemed to generate LSP parameter sequences of Vowel-Consonant-Vowel (VCV) units. The quality of synthesized speech by concatenation way of VCV units through table-look-up technique can not be improved so much owing to the distortion appearing on VCV units junction. In our method, the neural networks concatenate VCV units step by step with less distortion on VCV units junction, which synthesizes good quality speech.

A noise reduction filter composed of a sandglass-type neural network (Sandglass-type Neural network Noise Reduction Filter: SNNRF) was proposed in the present paper. Sandglass-type neural network (SNN) has symmetrical layer construction, and consists of the same number of units in input and output layers and less number of units in a hidden layer. It is known that SNN has the property of processing signals which is equivalent to KL expansion after learning. We applied the recursive least square (RLS) method to learning of SNNRF, so that the SNNRF became able to process on-line noise reduction. This paper showed theoretically that SNNRF behaves most optimally when the number of units in the hidden layer is equal to the rank of covariance matrix of signal component included in input signal. Computer experiments confirmed that SNNRF acquired appropriate characteristics for noise reduction from input signals, and remarkably improved the SN ratio of the signals.

We propose a method for embedding the awareness state and response state in an image-based avatar to smoothly and automatically start an interaction with a user. When both states are not embedded, the image-based avatar can become non-responsive or slow to respond. To consider the beginning of an interaction, we observed the behaviors between a user and receptionist in an information center. Our method replayed the behaviors of the receptionist at appropriate times in each state of the image-based avatar. Experimental results demonstrate that, at the beginning of the interaction, our method for embedding the awareness state and response state increased subjective scores more than not embedding the states.