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Deep acoustic models typically receive features in the first layer of thenetwork, and process increasingly abstract representations in the subsequentlayers. Here, we propose to feed the input features at multiple depths in theacoustic model. As our motivation is to allow acoustic models to re-examinetheir input features in light of partial hypotheses we introduce intermediatemodel heads and loss function. We study this architecture in the context ofdeep Transformer networks, and we use an attention mechanism over both theprevious layer activations and the input features. To train this model'sintermediate output hypothesis, we apply the objective function at each layerright before feature re-use. We find that the use of such iterated losssignificantly improves performance by itself, as well as enabling input featurere-use. We present results on both Librispeech, and a large scale videodataset, with relative improvements of 10 - 20% for Librispeech and 3.2 - 13%for videos.

Deja-vu: Double Feature Presentation and Iterated Loss in Deep Transformer Networks