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.

Artificial intelligence-based intelligent elderly care system

“…The speech synthesis function module, as the output part of the speech interaction model, uses advanced prosody analysis and synthesis algorithms to convert text sequences into sound waveforms. Subsequently, the electrical signal is converted into a sound signal through a voice playback device such as a loudspeaker to realize voice playback [7,8,9] .…”

Digital Transformation Based on AI Technologies in European Union Organizations

“…Speech recognition plays a critical role in promoting positive consumer experiences, as identified in a study [41]. However, some disadvantages exist, such as reduced voice recognition accuracy in certain acoustic conditions, difficulty recognizing fast or accented speech, and the possible need for continuous training algorithms to adapt to new voices and dialects [42,43]. Speech recognition is an evolving technology that has the potential to transform how people interact with devices and technology [44,45].…”

Automatic Speech Recognition for Human-Robot Interaction on The Humanoid Robot Barelang 7

“…Meanwhile, the development of HRI on more advanced robots is carried out by [7] and [8]. Robotic systems designed by [7] are used to help people who are in isolation when contracting infectious diseases.…”

“…On the other hand, research conducted by [8] develops HRI on servant robots. In this system, the sound signal is first preprocessed to eliminate noise and detect the presence or absence of the speaker's voice.…”