This paper investigates the combination of different acoustic features. Several methods to combine these features such as concatenation or LDA are well known. Even though LDA improves the system, feature combination by LDA has been shown to be suboptimal. We introduce a new method based on neural networks. The posterior estimates derived from the NN lead to a significant improvement and achieve a 6% relative better word error rate (WER). Results are also compared to system combination. While system combination has been reported to outperform all other combination techniques, in this work the proposed NN-based combination outperforms system combination. We achieve a 2% relative better WER, resulting in an improvement of 7% relative to the baseline system.

In addition to giving better recognition performance w.r.t. WER, NN-based combination reduces both, training and testing complexity. Overall, we use a single set of acoustic models, together with the training of the NN.