Abstract: Speech recognition technology has wide applications in intelligent design. The AP7003 IC is a high-performance and low-cost speech recognition circuit. This paper introduces its basic circuit and some applications in robot design, as well as the basic principles of speech recognition systems, providing a framework for the design of intelligent robots. Keywords: AP7003, speech recognition, intelligent robot, target word. Speech recognition technology takes language as its research object, involving multiple fields such as physiology, linguistics, computer science, and signal processing. It is an important research direction in language signal processing and has extremely broad application prospects in intelligent control, multimedia, and human-computer dialogue. 1 Speech Recognition Technology Speech recognition technology first needs to extract speech feature parameters, that is, extract useful information for speech recognition from language signals. The extraction of speech feature parameters needs to consider factors such as the fundamental tone at the selected word position, the spectrum of nasal consonants, the spectrum of vowels, and the spectrum of fricatives. Speech recognition technology also requires pattern matching and model training techniques, which mainly include dynamic time warping, HMM models, and artificial neural networks. Time warping is the process of transforming the time-varying features within a word into a uniform one. During time warping, the time axis of the word is unevenly distorted or bent to align its features with the model's characteristics. This technique is a powerful corrective measure and is extremely effective in improving the system's recognition accuracy. Artificial neural networks are adaptive nonlinear dynamic systems that simulate the basic principles of human brain activity, possessing abilities such as learning, skill development, judgment, comparison, and generalization. Furthermore, the selection of speech recognition units is also a crucial step in speech recognition. Speech recognition units include words, syllables, and phonemes. For Chinese, syllable units are primarily selected because Chinese is a monosyllabic language, while English is a polysyllabic language. Chinese has approximately 400 syllables, a relatively small number, making recognition easier. The AP7003 speech recognition circuit is a low-cost dedicated integrated circuit for speech recognition. It integrates a microphone amplifier, A/D converter, speech processor, and I/O controller. After preprocessing, it can recognize 12 different groups of words, each group lasting 1.5 seconds, and can recognize words or consecutive words. It can be widely used in toys, recognition-controlled systems, and automatic answering systems. The functional block diagram of AP7003 is shown in Figure 1. Table 1 lists the pin functions of AP7003. [align=center]Table 1 Pin Functions of AP7003[/align] The main features of AP7003 are as follows: • Built-in microphone amplifier • Built-in A/D converter • DIP40 dual in-line package • Capable of recognizing 12 groups of 1.5s-long words • I/O: 2 general purpose inputs, 4 trigger inputs, 2 output ports with 4 and 12 outputs respectively, 2 LED drivers. AP7003 has two operating modes: recording mode and recognition mode. Before entering recognition mode, the target word should be recorded into the circuit. AP7003 has 12 memory modules for storing 12 different phrases. Each memory module can store a 1.5s-long phrase. Target words can be recorded and stored by selecting memory modules via the keyboard or according to a programmed sequence. Voice can be recorded into the circuit via an external microphone or other media, processed internally, and stored in memory modules with different digital characteristic signals. After the target word is entered into the circuit, the working mode is switched to recognition mode. During operation, the circuit compares the current speech with the target word speech pre-recorded in the memory. If the speech features match, a high or low level is output at the corresponding output terminal of the circuit. 3 Voice-Controlled Robot Design Speech recognition circuits are widely used in intelligent control. Applying this circuit to the design of intelligent robots enables the robot to have the initial ability to converse with humans, and the operation of the robot is universal, greatly increasing the operator's interest. Figure 2 is the main principle block diagram of the robot circuit. The definitions of the buttons in Figure 2 are shown in Table 2. After power-on, the 12 memory cells must be cleared before the target word is entered. When the target word is entered, LED1 is enabled. After the entry is made, the circuit will enter the recognition mode. [align=center] Table 2 Definition of Buttons[/align] If the word entered by the microphone matches the original word in the memory, the corresponding POA output port is enabled and drives the circuit breaker, causing the motor to move. In this design, the robot mainly performs several actions: "Forward" (J1), "Backward" (J2), "Turn Left" (J3), "Turn Right" (J4), "Lift" (J5), "Lower" (J6), and "Stop". "Stop" is defined as `clearOutput`, and the others are defined as POA3, POA4, POA5, POA6, POA7, and POA8 respectively. Taking "Forward" (J1) and "Backward" (J2) as examples, as shown in Figure 3, when "Stop" is executed, all POA states are cleared, and the robot stops. Of course, the speech recognition circuit has certain requirements for the decibel value of the external microphone (MIC). Experience shows that a 56dB microphone is preferable in this speech recognition circuit. Additionally, the AP7003 operates at 2.4–4.5V. If your application system operates at 5V, then powering the AP7003 through a diode can ensure long-term stable and reliable operation of the system and also help reduce system power consumption. 4. Conclusion In practical applications, the microphone's decibel level and the Set R_Level matching degree settings have a significant impact on the speech recognition performance. If these two parameters are set appropriately, the circuit can achieve a high recognition rate, generally exceeding 80%, making it a highly effective speech recognition circuit.