This paper presents a new architecture for automatic continuous speech recognition called HEAR - Hybrid Episodic-Abstract speech Recognizer. HEAR relies on both parametric speech models (HMMs) and episodic memory. We propose an evaluation on the Wall Street Journal corpus, a standard continuous speech recognition task, and compare the results with a state-of-the-art HMM baseline. HEAR is shown to be a viable and a competitive architecture. While the HMMs have been studied and optimized during decades, their performance seems to converge to a limit which is lower than human performance. On the contrary, episodic memory modeling for speech recognition as applied in HEAR offers flexibility to enrich the recognizer with information the HMMs lack. This opportunity as well as future work are exposed in a discussion.