## 4 Experiments and Results
### Experimental Results and Analysis  
Tables 3 and 4 present the WER results on LibriSpeech database for the \"motor off\" or \"motor on\" state, respectively. The values in the first column of these two tables represent the recognition \begin{table}
\begin{tabular}{l l} \hline \hline
**Hardware** & **Specifications** \\ \hline Sensors & 360 degree sound localization \\ \hline Movement & Three full-revolute axes \\ \hline Sound & Two premium speakers \\ \hline Processor & High-end ARM-based mobile \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hardware specifications of JIBO [2]. performance in absence of the ego-noise and indicate the performance of the acoustic model used in all models. This acoustic model was trained using a kaldi [25] script and its performance is similar to that of a p-norm DNN trained on a 100 h subset published by kaldi. (6% to 9% and 20% to 28% on the \"clean\" and \"other\" test set, respectively) Furthermore, the values in the last column show the average performance at SNR of 5 dB and 15 dB. On the both cases, it is discovered that the motor-state data yields better recognition performances as reported in [14]. As we expected, the auxiliary features like the latent feature, generated by using the bottleneck layer, yielded superior performance when compared to the DNN [14] using one-hot encoded vectors. It indicates that the bottleneck layer can create more valuable representation of the motor-state data by fusing along with the spectral features and being compressed. In addition, the proposed model was superior to the spectral feature mapping, a DNN-based speech enhancement method. Figure 5 shows the results of the DNN-based spectral feature mapping. According to the evaluation by the following acoustic model, the reconstructed features did not bring a significant performance improvement. Because, the DNN for the spectral feature mapping was designed lighter than the usual DNN-based mapping model to have a similar amount of computation as the proposed preliminary network. For the comparison of the proposed algorithms, the WER was reported by changing the output features among senones, MS, and MFCC. When constructing the preliminary DNN. The results show that the senones are appropriate as the target features for the preliminary DNN. Hence, predicting the senones turns out most relevant for the task of the primary DNN. To be specific, the bDNN\({}_{senone}\) model achieved relative WER reduction of 2.24% and 5.83% over the baseline\({}_{1}\) model for the cases including \"motor off\" and \"motor on\" states. Furthermore, As for the position of latent features, the third layer yielded the superior performance for the bDNN\({}_{senone}\) showing the best results as displayed in Figure 6. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Preliminary DNN**} & \multicolumn{3}{c}{**Primary DNN**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Input Feature**} & \multicolumn{2}{c}{**Output Feature**} & \multicolumn{2}{c}{**Input Feature**} & \multicolumn{2}{c}{**Output Feature**} \\ \hline \multirow{3}{*}{Baseline} & baseline\({}_{1}\) & - & & Noisy MFCC & \\ \cline{2-7}  & baseline\({}_{2}\) & - & & Noisy MFCC and MS & \\ \cline{2-7}  & baseline\({}_{3}\) & Noisy MFCC & Clean MFCC & Reconstructed MFCC & \\ \hline \multirow{3}{*}{Proposed} & bDNN\({}_{senone}\) & \multirow{3}{*}{\(\text{senone}\)} & \multirow{3}{*}{\(\text{senone}\)} & \multirow{3}{*}{\(\text{benone}\)} \\ \cline{2-7}  & bDNN\({}_{MS}\) & & & MS & Noisy MFCC and BN & \\ \cline{1-1} \cline{3-7}  & bDNN\({}_{MFCC}\) & & clean MFCC & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model specifications (the MS and BN indicate the motor-state and latent feature, respectively).