Generating co-speech gestures for interactive digital humans remains challenging because of the indeterministic nature of the problem. The authors observe that gestures generated from speech audio or text by existing neural methods often contain less movement shift than expected, which can be viewed as slow or dull. Thus, a new generative model coupled with memory networks as dynamic dictionaries for speech-driven gesture generation with improved diversity is proposed. More specifically, the dictionary network dynamically stores connections between text and pose features in a list of key-value pairs as the memory for the pose generation network to look up; the pose generation network then merges the matching pose features and input audio features for generating the final pose sequences. To make the improvements more accurately measurable, a new objective evaluation metric for gesture diversity that can remove the influence of low-quality motions is also proposed and tested. Quantitative and qualitative experiments demonstrate that the proposed architecture succeeds in generating gestures with improved diversity.