Multimodal Attention Merging for Improved Speech Recognition and Audio Event Classification

Training large foundation models using self-supervised objectives on unlabeled data, followed by fine-tuning on downstream tasks, has emerged as a standard procedure. Unfortunately, the efficacy of this approach is often constrained by both limited fine-tuning compute and scarcity in labeled downstream data. We introduce Multimodal Attention Merging (MAM), an attempt that facilitates direct knowledge transfer from attention matrices of models rooted in high resource modalities, text and images, to those in resource-constrained domains, speech and audio, employing a zero-shot paradigm. MAM reduces the relative Word Error Rate (WER) of an Automatic Speech Recognition (ASR) model by up to 6.70 Audio Event Classification (AEC) model by 10.63 data/compute is available, we present Learnable-MAM, a data-driven approach to merging attention matrices, resulting in a further 2.90 WER for ASR and 18.42