Recently, some speech recognition methods using fusion of visual and auditory information have been researched. In this paper, a study on the mouth shape image suitable for fusion of visual and auditory information has been described. Features of mouth shape which are extracted from gray level image and binary image are adopted, and speech recognition using linear combination method has been performed. From results of speech recognition, the studies on the mouth shape features which are effective in fusion of visual and auditory information have been performed. And the effectiveness of using two kinds of mouth shape features also has been confirmed.

The high-speed and low-power system LSIs in recent years have crucial need for managing power supply noise so that it might not substantially affect the circuit functionality and performance. The decoupling capacitance is known as an effective measure for suppressing the power supply noise. In this paper, we propose a design methodology for decoupling capacitance budgeting, in which the decoupling capacitance is distributed appropriately over the LSI chip area in order to suppress the power supply noise of each local region. For efficient budgeting, we introduced a new concept of power-capacitance ratio, which is the ratio of power dissipation to capacitance. The proposed method first performs a simplified power supply noise analysis by using a lumped circuit model to determine the total required on-chip capacitance, and calculate the power-capacitance ratio. Then, in the layout design phase, the decoupling capacitance budgeting is performed by using the above power-capacitance ratio as a guideline. The effectiveness of the proposed method was verified by using SPICE simulations on example chip models of 90 nm technology node. The verification results show that, even for a chip with very wide on-chip variation in power density, the proposed method can suppress the power supply noise of each local region effectively.

We propose a speech recognition method using fusion of auditory and visual information for accurate speech recognition. Since we use both auditory information and visual information, we can perform speech recognition more accurately in comparison with the case of either auditory information or visual information. After processing each information by HMM, they are fused by linear combination with weight coefficient. We performed experiments and confirmed the validity of the proposed method.