Improved bottleneck features using pretrained deep neural networks

Bottleneck features have been shown to be effective in improving the accuracy of automatic speech recognition (ASR) systems. Conventionally, bottleneck features are extracted from a multi-layer perceptron (MLP) trained to predict context-independent monophone states. The MLP typically has three hidden layers and is trained using the backpropagation algorithm. In this paper, we propose two improvements to the training of bottleneck features motivated by recent advances in the use of deep neural networks (DNNs) for speech recognition. First, we show how the use of unsupervised pretraining of a DNN enhances the network’s discriminative power and improves the bottleneck features it generates. Second, we show that a neural network trained to predict context-dependent senone targets produces better bottleneck features than one trained to predict monophone states. Bottleneck features trained using the proposed methods produced a 16% relative reduction in sentence error rate over conventional bottleneck features on a large vocabulary business search task.