###### Abstract
built an articulatory dataset specifying in Chinese Mandarin [9] and investigated its efficacy in speech animation, and the dataset was created by Carstens EMA AG501 device. This real multi-channel speech data can be only used for specific structures but not for the simulation in any acoustic environment. [PERSON] et al. proposed a multi-channel room impulse responses dataset [10]. The room impulse responses were measured by three microphones in different locations of the room under three different reverberation conditions. Therefore, it can only be used for multi-channel simulation in these specific environments. [PERSON] et al. proposed a method for collecting the distant multi-channel speech and noise dataset [11]. The data were collected at four different distant positions in an indoor room, in which an artificial mouth was used for playing the clean source speech data and three kinds of multi-channel microphone arrays were used for recording the distant speech data. The dataset can be used for creating the simulated noisy speech data reflecting various indoor acoustic conditions corrupted by room reverberation and additive noise. However, its microphone array structure is fixed and cannot be adjusted for specific situations. [PERSON] et al. proposed a geometric sound simulation approach for generating and augmenting training data in speech-related machine learning tasks [12]. The method is capable of modeling occlusion, specular, and diffuse reflections of sound in the complicated acoustic environments. Spatialized Multi-Speaker Wall Street Journal (SMS-WS) [13] proposed by [PERSON] et al. is a multi-channel dataset of overlapping speech for training, evaluation, and the detailed analysis of source separation and extraction. It has a high degree of randomness w.r.t. room size, array center, and rotation, as well as speaker position. However, the microphone array in this dataset is only a circular array and cannot be changed, so it cannot be applied to the scenes that require a specific shape of the microphone array. [PERSON] et al. proposed a dataset [14] for evaluating continuous speech separation. In this dataset, the speech signal is continuous, containing both the overlapped and overlap-free components. However, only circular arrays are available in this data set, and no other array types are included. Although these methods recently proposed can better simulate the microphone array speech data in a real acoustic environment, most of them have certain limitations and cannot be arbitrarily changed according to the goal of the researcher, so this provides a direction for research on more adaptable microphone array speech simulator. In this paper, a _pyroomacoustics_-based microphone array speech simulator (MASS) in room acoustic environment is proposed. In the MASS, the _pyroomacoustics_ is extended to be able to add any kinds of isotropic spherically diffuse noise and set the corresponding signal-to-noise ratio (SNR) to better simulate real life scenes, such as meeting room acoustic environment. Through the analysis of the simulated microphone array speech and the experiments on the multi-channel speech coding and enhancement methods, it can be seen that the microphone array speech signals simulated by the MASS can well simulate the speech data in the real room acoustic environment, and can be applied in the research of related fields. The structure of the paper is organized as follows: The proposed microphone array speech simulator is described in Section 2. The simulation and analysis are given in Section 3. Application in multi-channel speech coding and enhancement are shown in Section 4. Finally, conclusions and future work are summarized in Section 5.