## 4 Experiments and Results
### Experimental Setup  
In our experiments, the Kaldi toolkit [25] was utilized to train both the preliminary DNN extracting the latent feature and the primary DNN for acoustic modeling. The two systems were equally designed with 4 hidden layers and each layer has 512 hidden units except for the bottleneck layer. For the primary DNN, the rectified linear unit (ReLU) activation functions was used in the lower layers and the softmax function was used at the output layer. For the acoustic features, 13 dimensional MFCC features were extracted using 25 ms analysis window with 10 ms frame shift. The baseline models used for comparison with the proposed model in this experiment are described in Table 2. Three baseline models were adopted to evaluate the proposed model. As for the first baseline system, the MFCC features stacked with 11 adjacent frames were used as the DNN input for acoustic modeling. In the second baseline system, the MFCC features and one-hot representation of the motor-state were used as the DNN input with 165 dim (\(15\times 11\)). Furthermore, the third baseline system is a DNN-based spectral mapping system designed to have a total of four hidden layers in order to have similar computational complexity to our proposed model. The spectral mapping model is trained using the 100 h subset contaminated by the ego-noises (5 dB and 15 dB SNR). The enhanced MFCC features are evaluated through the following acoustic model which is identical to the first baseline model. Therefore, the performance of the DNN-based spectral mapping model was verified through evaluation of the reconstructed MFCC features. We measured the performance of the first baseline model to check the performance of the acoustic model to be used in all experiments. In addition, to improve speech recognition performance in an environment where the ego-noise exists, the second and third baseline models use auxiliary features or a feature enhancement model, respectively. Therefore, evaluation in a clean environment without the ego-noise was performed only in the first baseline model. As for the proposed systems, the preliminary DNN was separately constructed with 4 hidden layers at the early stage. To investigate the effect of the various latent features, experiments were performed by varying output features, and bottleneck layer positions. Firstly, the senones, motor-state label and original MFCC features were independently placed in order at the output, then three kinds of the primary DNN yielding the latent features (bDNN) were designed including the bDNN\({}_{senone}\), bDNN\({}_{MS}\), bDNN\({}_{MFCC}\). The sigmoid and tanh activation functions were used for the classification and regression task, respectively. In addition, we varied the placement of the bottleneck layer from the bottom hidden layer (position 1) to the top hidden layer (position 4). For all the preliminary networks, the stacked MFCC and motor-state features (\((13+2)\times 11=165\)-dim.) were used for the input. Then, the extracted motor-state dependent latent features were combined with the MFCC again and used to train the acoustic model as shown in Figure 4. The complexity of all models is described in Table 1.