# Voice Interaction Recognition Design in Real-Life Scenario Mobile Robot Applications
###### Abstract  
This paper designed a voice interactive robot system that can conveniently execute assigned service tasks in real-life scenarios. It is equipped without a microphone where users can control the robot with spoken commands; the voice commands are then recognized by a well-trained deep neural network model of automatic speech recognition (ASR), which enables the robot to execute and complete the command based on the navigation of a real-time simultaneous localization and mapping (SLAM) algorithm. The voice interaction recognition model is divided into two parts: (1) speaker separation and (2) ASR. The speaker separation is applied by a deep-learning system consisting of eight convolution layers, one LSTM layer, and two fully connected (FC) layers to separate the speaker's voice. This model recognizes the speaker's voice as a referrer that separates and holds the required voiceprint and removes noises from other people's voiceprints. Its automatic speech recognition uses the novel sandwich-type conformer model with a stack of three layers, and combines convolution and self-attention to capture short-term and long-term interactions. Specifically, it contains a multi-head self-attention module to directly convert the voice data into text for command realization. The RGB-D vision-based camera uses a real-time appearance-based mapping algorithm to create the environment map and replace the localization with a visional odometer to allow the robot to navigate itself. Finally, the proposed ASR model was tested to check if the desired results will be obtained. Performance analysis was applied to determine the robot's environment isolation and voice recognition abilities. The results showed that the practical robot system successfully completed the interactive service tasks in a real environment. This experiment demonstrates the outstanding performance with other ASR methods and voice control mobile robot systems. It also verified that the designed voice interaction recognition system enables the mobile robot to execute tasks in real-time, showing that it is a convenient way to complete the assigned service applications. automatic speech recognition; voice interaction; mobile robot; simultaneous localization and mapping (slam); deep learning Article  
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The topic of human-computer interaction has always been of great interest to everyone in the applications of the mobile robot [1], and speech-related technologies are also heavily used in this topic. Human-computer interaction is the interaction and communication between humans and machines. The most intuitive and simplest communication of humans is through speech. In the past, humans have relied on interfaces to operate robots, and it takes time for the elderly or children to learn, and become familiar with how to operate them. If the robot can directly listen and follow the user's voice command to perform service tasks, it will be a lot easier and more convenient for humans. For a more complete description in the advantages of human-robot interaction, please refer to [2]. This research withdrew from the traditional manual interface, but focused on directly controlling the robot through a voice command to perform specific tasks. A human-interactive robot is an inevitable trend. A service robot that has functions of automatic speech recognition (ASR), natural language understanding, image recognition, and interactive communication will approach future development. The simplest case of the speaker separation model is to disentangle two mixed voice signals with the same dimension of input and output. In recent years, deep clustering [3], TasNet [4; 5], and permutation invariant training (PIT) [6] models have become popular speaker separation models. Below is a brief discussion of these models. Deep clustering uses the concept of k-means clustering, which first converts a sound signal into a spectrum and then, translates the spectrum into a vector.