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Attention layers are an integral part of modern end-to-end automatic speechrecognition systems, for instance as part of the Transformer or Conformerarchitecture. Attention is typically multi-headed, where each head has anindependent set of learned parameters and operates on the same input featuresequence. The output of multi-headed attention is a fusion of the outputs fromthe individual heads. We empirically analyze the diversity betweenrepresentations produced by the different attention heads and demonstrate thatthe heads become highly correlated during the course of training. Weinvestigate a few approaches to increasing attention head diversity, includingusing different attention mechanisms for each head and auxiliary training lossfunctions to promote head diversity. We show that introducingdiversity-promoting auxiliary loss functions during training is a moreeffective approach, and obtain WER improvements of up to 6% relative on theLibrispeech corpus. Finally, we draw a connection between the diversity ofattention heads and the similarity of the gradients of head parameters.

Analysis of Self-Attention Head Diversity for Conformer-based Automatic Speech Recognition