It is well known that KT method proposed by R. Kumaresan and D. W. Tufts is used as a popular parameter estimation method of exponentially damped signal. It is based on linear backward-prediction method and singular value decomposition (SVD). However, it is difficult to estimate parameters correctly by KT method in the case when high noise exists in the signal. In this paper, we propose a parameter (frequency components and damping factors) estimation method to improve the performance of KT method under high noise. In our proposed method, we find the signal zero groups by calculating zeros with different data record lengths according to the combination of forward-prediction and backward-prediction, the mean value of the zeros in the signal zero groups are calculated to estimate the parameters of the signal. The proposed method can estimate parameters correctly and accurately even when high noise exists in the signal. Simulation results are shown to confirm the effectiveness of the proposed method.

This paper analyses the features required to efficiently recognize five Japanese vowels for lip-reading. Various features, such as shape and radius, are calculated from the lip region and fed to the k Nearest Neighbor method. We calculated 15 feature sets and found that the feature set including the area and aspect ratio of the mouth cavity is effective for Japanese vowel recognition.

This paper describes a recognition method of Japanese single sounds for application to lip reading. Related researches investigated only five or ten sounds. In this paper, experiments were conducted for 45 Japanese single sounds by classifying them into five vowels category, ten consonants category, and 45 sounds category. We obtained recognition rates of 94.7, 30.9 and 30.0% with trajectory feature.

In this paper, we present a new signal frequency estimation method based on the sinusoidal additive synthesis model. In the proposed method, frequencies in both the signal and noise are estimated with several delay times by using an expanded linear prediction (LP) method, and assuming that the signal is stationary and noise is unstationary in short record length. Frequencies in the signal are extracted according to their dependence on different delays. The frequency estimation can be accomplished with short record length even in the case where the number of frequency components in the signal is unknown. And it is capable of estimating the frequencies of a signal in the presence of noise. Furthermore, the proposed method estimates the parameters with less computation and high estimation accuracy. Simulation results are provided to confirm the effectiveness of the proposed method. The comparison of estimation accuracy between the proposed method and the analysis by synthesis (ABS) method is shown with the corresponding Cramer-Rao lower bound. And the frequency resolution of this method is also shown.