Acoustic modeling using bidirectional gated recurrent convolutional units

Abstract

Convolutional and bidirectional recurrent neural networks have achieved considerable performance gains as acoustic models in automatic speech recognition in recent years. Latest architectures unify long short-term memory, gated recurrent unit and convolutional neural networks by stacking these different neural network types on each other, and providing short and long-term features to different depth levels of the network. For the first time, we propose a unified layer for acoustic modeling which is simultaneously recurrent and convolutional, and which operates only on short-term features. Our unified model introduces a bidirectional gated recurrent unit that uses convolutional operations for the gating units. We analyze the performance behavior of the proposed layer, compare and combine it with bidirectional gated recurrent units, deep neural networks and frequency-domain convolutional neural networks on a 50 hour English broadcast news task. The analysis indicates that the proposed layer in combination with stacked bidirectional gated recurrent units outperforms other architectures.