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The goal of a speech-to-image transform is to produce a photo-realisticpicture directly from a speech signal. Recently, various studies have focusedon this task and have achieved promising performance. However, currentspeech-to-image approaches are based on a stacked modular framework thatsuffers from three vital issues: 1) Training separate networks istime-consuming as well as inefficient and the convergence of the finalgenerative model strongly depends on the previous generators; 2) The quality ofprecursor images is ignored by this architecture; 3) Multiple discriminatornetworks are required to be trained. To this end, we propose an efficient andeffective single-stage framework called Fusion-S2iGan to yield perceptuallyplausible and semantically consistent image samples on the basis of givenspoken descriptions. Fusion-S2iGan introduces a visual+speech fusion module(VSFM), constructed with a pixel-attention module (PAM), a speech-modulationmodule (SMM) and a weighted-fusion module (WFM), to inject the speech embeddingfrom a speech encoder into the generator while improving the quality ofsynthesized pictures. Fusion-S2iGan spreads the bimodal information over alllayers of the generator network to reinforce the visual feature maps at varioushierarchical levels in the architecture. We conduct a series of experiments onfour benchmark data sets, i.e., CUB birds, Oxford-102, Flickr8k andPlaces-subset. The experimental results demonstrate the superiority of thepresented Fusion-S2iGan compared to the state-of-the-art models with amulti-stage architecture and a performance level that is close to traditionaltext-to-image approaches.

Fusion-S2iGan: An Efficient and Effective Single-Stage Framework for Speech-to-Image Generation