Highly anthropomorphic voice interaction algorithm and emotion interaction algorithm for robot and robot
A technology for voice interaction and emotional communication, applied in the field of intelligent robots, it can solve the problems of unnatural robot interaction and process, and achieve the effect of convenient application, no noise interference, and reducing the influence of surrounding noise.
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specific Embodiment 1
[0053] Robot technology based on highly anthropomorphic human-computer voice interaction realizes voice interaction through the following processes:
[0054]1. When the robot is powered on, start the voice wake-up algorithm in the embedded chip, and the algorithm will run all the time, monitoring whether the user uses the wake-up word to wake up.
[0055] 2. After the user uses the wake-up word, the robot first uses the sound source positioning algorithm according to the user's voice to determine the direction and distance of the user. The head of the robot turns, aligns with the user according to the user's direction, starts the camera, and starts face recognition and track.
[0056] At the same time, the large-scale continuous speech recognition system starts to recognize the user's subsequent voice commands. If there is still the user's voice V after the wake-up word spoken by the user before the large-scale continuous speech recognition system starts, wait for the large-sc...
specific Embodiment 2
[0068] 1) The robot recognizes the text spoken by the user and generates a text sequence: A1, A2, A3, ..., An (here, for Chinese, An can be a Chinese character, such as "中", "国", etc., or it can be Pinyin, such as "zh", "ong1", etc.);
[0069] 2) By calculating whether the multivariate probability P(A1, A2, A3, ..., An) reaches the threshold, the robot detects whether the text sequence A1, A2, A3, ..., An is valid speech; Meta-conditional probability P(A2|A1), P(A3|A2), ... , then P(A1, A2, A3, ..., An) =P(A1)* P(A2|A1)* P(A3 |A2)....* P(An|An-1), the conditional probability of three or more variables is similar;
[0070] 3) If the robot detects that the text sequence is non-speech, it will not respond;
[0071] 4) If the robot detects that the text sequence is speech, it will use the semantic processing module to judge whether the user is talking to the robot (for example, to detect the matching threshold between the text sequence and the robot’s preset question), if it is ...
specific Embodiment 3
[0093] 1) Create a user's attribute list, which includes two categories, one is category, such as what the user likes and dislikes; the other is level category, such as language level (for example, the third grade of kindergarten is 30), mathematics level (for example, The level of the second grade of elementary school is 20), swimming level; this attribute list is established in two ways, one is to give options and let the user fill in; the other is to extract the user's information in the daily voice interaction of the robot, and automatically create it for the user .
[0094] 2) Analyze the user's current question (including the current question and the previous question), see if there is any content in the user attribute list, if not, answer in a normal tone.
[0095] 3) If the analysis of the user's question includes the attribute of whether it is a category, an emotional answer voice is given according to the user's preference.
[0096] 4) If the user's problem is analy...
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