- Tomohiro Tanaka, T. Shinozaki, Shinji Watanabe, Takaaki Hori. 2016. Evolution Strategy Based Neural Network Optimization and LSTM Language Model for Robust Speech Recognition. Abstract: This paper reports our system for the 1-channel track task in the 4th CHiME challenge (CHiME4). A bottle-neck in developing neural network based systems is the tuning of meta-parameters. We automate it by using Covariance Matrix Adaptation Evolution Strategy (CMA-ES) so that high performance system is obtained without relying on human experts. We run two evolution experiments for the DNN acoustic model used in the official baseline system. One uses development set word error rate (WER) after the cross-entropy (CE) based training as the objective function for the evolution, and the other uses the WER after the sequential discriminative training. Additionally, we run an evolution experiment for a Long Short-Term Memory recurrent neural network based language model (LSTM-LM), replacing the original recurrent neural network language model (RNN-LM) used in the baseline system for N-best rescoring. All of these evolution experiments resulted in reduced WERs. To produce the final results, we augmented training data by pooling speech data from all the 6 channels and imported the optimized meta-parameter settings without modification. For the real test data, reduced WER of 17.40% and 16.58% were obtained compared to the baseline WER of 22.75% when the RNN and LSTM-LMs were used, respectively.
