Speech recognition systems have been applied in various fields due to recent development of digital signal processing techniques. For speech recognition in real circumstances, some countermeasure methods for surrounding noises are indispensable. In previous study, we derived an algorithm to estimate speech signals by using air-conducted sound mixed with noise as observation and using bone-conducted sound. At that time, a model of the observed air-conducted sound was represented by a simple additive model of the sound pressure level of the speech signal and the noise. In this paper, a signal processing method to estimate the speech signal is proposed by observing air-conducted speech under existence of surrounding noise and using bone-conducted speech. More specifically, an estimation algorithm reflecting higher order statistics of variables is derived by applying Bayes' theorem after introducing a stochastic model for the speech signal, air-and bone-conducted speeches. Furthermore, by applying the proposed method to speech signals measured under existence of surrounding noise, its effectiveness is confirmed.
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