Multimodal　word　discovery　(MWD)　is　often　treated　as　a　byproduct　of　the　speech-to-image　retrieval　problem.　However,　our　theoretical　analysis　shows　that　some　kind　of　alignment/attention　mechanism　is　crucial　for　a　MWD　system　to　learn　meaningful　word-level　representation.　We　verify　our　theory　by　conducting　retrieval　and　word　discovery　experiments　on　MSCOCO　and　Flickr8k,　and　empirically　demonstrate　that　both　neural　MT　with　self-attention　and　statistical　MT　achieve　word　discovery　scores　that　are　superior　to　those　of　a　state-of-the-art　neural　retrieval　system,　outperforming　it　by　2%　and　5%　alignment　F1　scores　respectively.　©　2021　IEEE