We propose a new scheme for speaker-dependent silent speech recognition systems (SSRSs) using both single-trial electroencephalograms (EEGs) scalp-recorded and speech signals measured during overtly and covertly speaking "janken" and "season" in Japanese. This scheme consists of two phases. The learning phase specifies a Kalman filter using spectrograms of the speech signals and independent components (ICs), whose equivalent current dipole source localization (ECDL) solutions were located mainly at the Broca's area, of the EEGs during the actual speech. In case of the "season" task, the speech signals were transformed into vowel and consonant sequences, and these relationships were learned by hidden Markov model (HMM) with Gaussian mixture densities. The decoding phase predicts spectrograms for the silent "janken" and "season" using the Kalman filter with the EEGs during the silent speech. For the silent "season", the predicted spectrograms were inputted to the HMM, and which "season" was silently spoken was determined by the maximal log-likelihood among each HMM. Our preliminary results as training steps are as follows: the silent "jankens" were correctly discriminated; the silent "season"-HMMs worked well, suggesting that this scheme might be applied to the discrimination between all the pairs of the hiraganas.