A chat robot system and chat method based on fuzzy neural network
A fuzzy neural network and chat robot technology, applied in the chat robot system and chat field, can solve problems such as difficulty in meeting chat needs, chat robots cannot learn, chat robot systems and methods do not appear, and achieve high-speed search and optimization error learning ability. Effect
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Embodiment 1
[0058] Such as figure 1 Shown, a kind of chat robot system based on fuzzy neural network, said system comprises:
[0059] The voice collection terminal 100 is installed on both sides of the chat robot head, and is used to receive dialogue information in the chatting process between the user and the chat robot (picking up the voice information in the chatting process in real time),
[0060] It adopts professional and sensitive symmetrical double pickup, which is composed of microphone, audio amplifier circuit, signal conditioner in sound card, sample holder and analog-to-digital converter. The obtained sound analog information is converted into a digital signal and stored in the disk. The microphone uses an electret condenser microphone, which works by utilizing a polymeric material diaphragm with permanent charge isolation. Using symmetrical double pickups, the position of the microphone determines the range of incoming sound, using acoustic phenomena to determine the positi...
Embodiment 2
[0163] Such as image 3 Shown: a kind of method that adopts above-mentioned chat robot system to carry out chatting, and this method comprises the following steps:
[0164] Step 1: After the robot is started, perform system initialization and self-inspection, and initially prevent the system from malfunctioning during the chat process.
[0165] Step 2: When the user chats with the robot, the initialization of the system collection mode has been completed, and the user's voice information is collected. The collected voice data has two purposes:
[0166] One, it is stored in fuzzy neural network training database 106 as input data in order to form fuzzy neural network learning sample,
[0167] 2. It is provided to the voice preprocessing module 101 for voice data preprocessing such as sampling, noise removal, endpoint detection, pre-emphasis, and windowing and framing.
[0168] Step 3: Extract feature parameters (sound intensity, loudness, pitch, period and pitch frequency) fr...
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