# Voice Interaction Recognition Design in Real-Life Scenario Mobile Robot Applications
###### Abstract
With research, the disadvantages of traditional automatic speech recognition model training have been improved and the end-to-end model and sequence-to-sequence model were found to be reasonable and effective solutions. For example, in 2012, Google developed the RNN-Architecture [8], which incorporates the memory feature of the recurrent neural network (RNN) into the ASR model. Not only did it eliminate the need to align audio and text, but it also achieved an end-to-end effect, jointly optimizing the parameters of the model and allowing the selection of the best phrase for the language model. In 2017, Google added the well-developed attention memory model to the ASR model and developed the LAS model, which can input a string of data and output the data together. Since its development, it has become popular in the field of speech [9]. The sequence-to-sequence deep-learning models are the leading architecture to generate voice recognition applications. Specifically, multiple layers of self-attention blocks are a powerful tool to encode acoustic features [10; 11; 12; 13]. The encoded features are then decoded into word sequences using recurrent transducers [14] or attention-based models [8] for the ASR applications. This paper selected a novel sandwich-type conformer model to approach the generic function of the ASR system to control the mobile robot. Simultaneous localization and mapping (SLAM) has been an important research topic in the field of robot navigation in the last decade. It offers map generation and self-localization abilities to greatly improve the working efficiency of robots in an unknown environment and to avoid collision problems. Nowadays, many types of sensors are proposed to build maps. The most popular laser sensors, such as the two-dimension laser Hector SLAM [15], combine various algorithms to approach the target of SLAM. This paper used a camera-type sensor in the SLAM application called the vision-based simultaneous localization and mapping (vSLAM) [16; 17]. The vSLAM has the advantage of a lower hardware cost and bountiful information. The monocular-type camera only catches a two-dimensional picture and cannot realize the distance of an object. Due to the fixed axle spacing between two camera lenses, a binocular-type camera uses two camera lenses to capture two pictures and fuses them to calculate the depth of the object. However, it is difficult to perfectly position the camera lens and it consumes high computation resources. The RGBD-type camera, which uses three color channels (RGB) to capture bountiful information, and the infrared structured light or time of fly (TOF) are used to directly obtain the three-dimensional information (including depth). The efficiency of the RGBD-type camera is very similar to the laser radar. This paper applied the robot operating system (ROS) platform to develop the robot system. ROS features a distributed frame to generate multiple communications in a topic, service, etc. ROS is compatible with many programs and languages, such as C++, Python, and JavaScript, with a topic or service module to communicate and develop applications. Another advantage of ROS is that it offers a higher integration and systematically builds individual module examination and verification. This paper designed an interactive human-robot system with a voice command to control a mobile robot and enable it to complete service tasks. The deep-learning neural network structure was employed for analysis and to allow the mobile robot equipped with an ASR to make an appropriate decision. The mobile robot monitors the current state of the task to approach the required action. A real-time appearance-based mapping (RTAB-MAP) algorithm [17] with a visual odometer was used to construct the three-dimensional maps and allow the robot to gradually move into the desired position in the service environment.