## 1 Introduction  
Voice as a form of communication can be acquired virtually everywhere and is the most natural and intuitive means of communication between humans or humans and machines. Recently, the humanoid robots such as Softbank's Pepper [1], MIT's home robot JIBO [2], and Intel's Jimmy [3] have been developed thanks to the considerable advances in the automatic speech recognition (ASR) technology. Many recent studies on this ASR technology, as an indispensable part for the humanoid robots, have been actively carried out, but there still retains a challenging problem of isolating ambient noise. The humanoid robots, additionally, generate the strong ego-noise [4], which results in a significant factor deteriorating the recognition performance. Indeed, since the distance between the microphone and the internal parts of the robot that generate the ego-noise is much closer than the distance between the microphone and the speaker, the ego-noise is loudly recorded into the microphone causing performance degradation. The robustness to the ambient noise has been one of the main issues in ASR technology to this day, but the robustness to the ego-noise has not been fully addressed. In general, DNN-based speech enhancement systems for dealing with the ambient noise are trained using two types of targets including masking-based targets and mapping-based targets. In the first case, the model attempts to describe the time-frequency (T-F) relationships of clean speech to background interference and make a binary classification decision on T-F units, such as ideal binary mask estimation [5]. Furthermore, in [6], DNNs were used to estimate a smoothed ideal ratio mask (IRM) in the Mel frequency domain for robust ASR. In the second case, the DNN learns a complex mapping function from noisy to clean speech using multi-condition training data [7; 8; 9]. However, such conventional DNN-based speech enhancement systems generally require very deep models or large databases to cope with as much ambient noise as possible, which is not suitable for embedded systems such as the humanoid robots. In addition, these systems have limitations in using motor-state information as an auxiliary feature. As for the ego noise suppression, spectral subtraction [10] can be one possible way. A DNN-based framewise prediction approach is developed to estimate the noise spectra using angular velocity when a robot manipulates a joint [11]. The estimated noise spectra are then subtracted from the target signal spectra to clean up the noisy speech signal. One of the problems in this approach is that the ASR performance gets quite poorer since the noise power estimates are different from the original ones especially when the ego-noise is non-stationary. Several researchers have tackled this problem by predicting and subtracting the ego-noise using templates. For instance, [12] proposed a way to predict the ego-noise using motion-like gesture and walking pattern template obtained from a pre-recorded motor noise that corresponds to the motion pattern. Along with the labeled motion command, the appropriate ego-noise template matched to the latest motion is selected from the template database and used for subtraction. A small set of noise template database are extended in [4] to a larger ego-noise space in which the template database is enhanced by incorporating more information related to the joints such as angular positions, velocities and accelerations. Next, [13] employed the motor data to use the intrinsic harmonic structure of the ego-noise and incorporated the ego-noise harmonics into a multichannel dictionary-based approach for the ego-noise reduction. These studies on the ego-noise reduction have shown that the instantaneous motor data of the humanoid robots can be used as a secondary information source for dealing with the ego-noise. Recently, we originally devised an idea in [14] to use the motor on/off state as the auxiliary information when designing the acoustic model of the DNN-based speech recognition system for the humanoid robots.