Wearable human-computer interactions in daily life are increasingly encouraged by the prevalence of intelligent wearables. It poses a demanding requirement of micro-interaction and minimizing social awkwardness. Our previous work demonstrated the feasibility of recognizing silent commands through around-ear biosensors with the limitation of user adaptation. In this work, we ease the limitation by a personalization framework that integrates spectral factorization of signals, temporal confidence rejection and commonly used transfer learning algorithms. Specifically, we first empirically formulate the user adaptation issue by presenting the accuracies of applying transfer learning algorithms to our previous method. Second, we improve the signal-to-noise ratio by proposing the supervised spectral factorization method that learns the amplitude and phase mappings between around-ear signals and the signals of articulated facial muscles. Third, we leverage the time continuity of commands and introduce the time decay into confidence rejection. Finally, extensive experiments have been conducted to evaluate the feasibility and improvements. The results indicate an average accuracy of 92.38% which is significantly larger than solely using transfer learning algorithms. And a comparable accuracy can be achieved with significantly reduced data of new users. The overall performance shows the framework can significantly improve the accuracy of user adaptations. The work would aid a further step toward commercial products for silent command recognition and inspire the solution to the user adaptation challenge of wearable human-computer interactions.