This paper introduces a new model for automatic speech recognition (ASR) called TEMM - Temporal Episodic Memory Model. TEMM is derived from a simulation of human episodic memory called Minerva2, and it not only overcomes the inability of Minerva2 to use temporal sequence for recognition flexibly, but it also employs a prediction mechanism as an additional source of information. The performance of TEMM on an ASR task is compared to state-of-the-art HMM/GMM baseline systems, and a first analysis shows both promising results and a need to further stabilise the consistency of the output of the new model.