With the increasing integration of advanced technology into military applications, weapons and equipment are evolving toward higher levels of sophistication. However, traditional military training faces significant challenges, such as long training durations, high costs, and limited training environments. These limitations often prevent traditional exercises from achieving their intended effectiveness and fail to meet the demands of modern military operations. To address these issues, imitation-based training systems have emerged as a practical solution.
This article introduces a smart voice-interactive teaching and playback system designed to simulate an operator’s actions during training. The teaching component provides operators with a clear and visual demonstration of the correct procedures, significantly reducing practice time and improving overall performance. Meanwhile, the playback system records key elements like voice commands, sound intensity, movements, timing, and operational appearance during training. This data is then replayed for review, enabling operators to identify and correct mistakes efficiently. The system does not rely on virtual reality technologies and can be implemented on a compact embedded platform, making it both cost-effective and versatile.
**1. System Principle**
The imitation exerciser consists of a central control computer and multiple slave devices, as illustrated in Figure 1. Here, only one slave device is described. Its hardware includes a measurement and control computer, an Arduino Mega2560 controller, a voice recognition module, a sound intensity detection unit, a voice synthesis component, a panel control unit, and an instrument panel. The panel control unit contains complex circuitry that manages various functions. During simulation, the slave device operates under the control of the Arduino Mega2560, executing the entire training process. In the teaching and playback mode, the system focuses on simulating the operation sequence without delving into detailed circuit designs.
The voice recognition module identifies the operator's command, while the sound intensity detection unit helps determine which operator is speaking. The Arduino Mega2560 monitors all panel components to detect user actions and records the training session. Each instrument's appearance is pre-programmed based on the required action, eliminating the need for real-time recording. During playback, the measurement and control computer reproduces the recorded process via the corresponding slave device.
**2. Unit System Planning**
**2.1 Voice Recognition Unit Planning**
Speech recognition technology has advanced rapidly, with two main categories: speaker-dependent and speaker-independent. Speaker-dependent systems require specific voice samples for training, whereas speaker-independent systems can recognize a wide range of voices without prior training. The LD3320 chip used in this system is a speaker-independent speech recognition module, offering a complete single-chip solution for voice recognition, control, and human-machine interaction. It allows dynamic keyword editing and supports efficient integration with microcontrollers.
The ATmega168 microcontroller controls the LD3320, managing all voice-related tasks and transmitting results to the Arduino Mega2560 via serial communication. The register-based interface of the LD3320 supports both parallel and SPI modes, with the parallel method being used here for direct I/O connection.
The voice recognition process follows a suspension-based approach, including initialization, keyword writing, recognition start, and response handling. The program is developed using the Arduino IDE and uploaded via the serial port after debugging.
**2.2 Sound Intensity Detection Unit Planning**
To identify which operator is speaking, a sound intensity detection unit is integrated. This circuit measures relative sound levels rather than absolute decibels. A capacitive microphone converts sound signals into electrical signals, which are amplified by the NE5532 op-amp. The signal is then converted to a DC voltage using an AC/DC RMS circuit before being sampled by the Arduino’s ADC. When the sound level exceeds a threshold, a transistor triggers an interrupt, initiating an ADC sample. The data is filtered and sent to the measurement and control computer upon request.
**2.3 Voice Component Planning**
Text-to-speech (TTS) technology enables real-time voice generation without pre-recorded files, reducing maintenance efforts. The SYN6658 TTS chip communicates via UART or SPI and is controlled by the MCU or PC. The chip receives text data, converts it to speech, and outputs the signal through an LM386 amplifier connected to a speaker.
Initial setup includes selecting the speaker, adjusting speech rate, tone, and volume. Different slave devices use distinct voices and parameters to distinguish between users. After initialization, the system waits for commands from the measurement and control computer.
**3. System Software Planning**
The software architecture includes a C#-based control program on the measurement and control computer and firmware on each Arduino Mega2560. The control program records critical data such as passwords, actions, timing, and appearances, using event codes to streamline the process. Data is recorded every 50 ms, and during playback, the system synchronizes with the recorded timeline to trigger actions accurately.
The Arduino Mega2560 handles commands from the control computer, processes voice recognition results, collects sound intensity data, and manages the TTS module. It uses serial interrupts for command reception and returns status updates. If no response is received within a set time, the system restarts the process.
By integrating a smart voice chip, this system offers an effective, low-cost alternative to traditional training methods. It requires no virtual reality support and can run on small embedded platforms, making it ideal for portable and field-use scenarios.
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