  Systems that can find correspondences between multiple modalities, such as
between speech and images, have great potential to solve different recognition
and data analysis tasks in an unsupervised manner. This work studies multimodal
learning in the context of visually grounded speech (VGS) models, and focuses
on their recently demonstrated capability to extract spatiotemporal alignments
between spoken words and the corresponding visual objects without ever been
explicitly trained for object localization or word recognition. As the main
contributions, we formalize the alignment problem in terms of an audiovisual
alignment tensor that is based on earlier VGS work, introduce systematic
metrics for evaluating model performance in aligning visual objects and spoken
words, and propose a new VGS model variant for the alignment task utilizing
cross-modal attention layer. We test our model and a previously proposed model
in the alignment task using SPEECH-COCO captions coupled with MSCOCO images. We
compare the alignment performance using our proposed evaluation metrics to the
semantic retrieval task commonly used to evaluate VGS models. We show that
cross-modal attention layer not only helps the model to achieve higher semantic
cross-modal retrieval performance, but also leads to substantial improvements
in the alignment performance between image object and spoken words.
