Lattice based optimization of bottleneck feature extractor with linear transformation

This paper proposes a lattice-based sequential discriminative training method to extract more discriminative bottleneck features. In our method, the bottleneck neural network is first trained with cross entropy criteria, and then only the weights of bottleneck layer are retrained with sequential criteria. If the outputs of the layer before bottleneck are treated as the raw features, the new method is an equivalent to a linear feature transformation algorithm. This linearity makes the optimization much easier than updating the whole neural network. Just like the fMPE and RDLT, the neural network is retrained with batch mode gradient descent, making the training to be easily implemented in parallel. Meanwhile, batch mode optimization can naturally deal with the indirect gradient to make the optimization more precise. Experimental results on a Mandarin transcription task and the Switchboard task have shown the effectiveness of the proposed method with the CER decreases from 12.2% to 11.3% and the WER from 16.1% to 15.0%, respectively.