Water Dispenser Voice Control Using Container-Aware AI Sensitivity
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Solution Overview
Problem
Conventional water purifiers using speech recognition technology often malfunction due to noise interference in the environment, leading to misrecognition of speech commands, and lack customization based on user behavior.
Innovation Solution
An artificial intelligence apparatus that determines whether a container is seated on a water dispensing apparatus using weight data and adjusts speech recognition sensitivity, and trains an AI model using usage history information to enhance speech recognition accuracy and provide personalized water dispensing functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If speech recognition is activated continuously to improve user convenience, then ease of operation is improved, but misrecognition errors increase due to noise interference
Solution Approach 1:
The speech recognition sensitivity is dynamically adjusted based on the detected container state. When a container is detected, the system operates in high-sensitivity mode to capture user commands even in noisy environments. When no container is present, the system switches to low-sensitivity mode to prevent misrecognition, thus adapting the recognition behavior to operational context.
Solution Approach 2:
The system changes the speech recognition sensitivity parameter according to the container presence state. This parameter adjustment allows the system to optimize between capturing weak speech signals when a container is present and filtering out noise when no container is detected, resolving the contradiction between ease of operation and recognition accuracy.
2Ease of operation
If speech recognition sensitivity is increased to capture more speech commands, then ease of operation is improved, but misrecognition errors increase due to noise and similar speech
Solution Approach 1:
The speech recognition sensitivity is dynamically adjusted based on the detected container state. When a container is detected, the system operates in high-sensitivity mode to capture user commands even in noisy environments. When no container is present, the system switches to low-sensitivity mode to prevent misrecognition, thus adapting the recognition behavior to operational context.
Solution Approach 2:
The system changes the speech recognition sensitivity parameter according to the container presence state. This parameter adjustment allows the system to optimize between capturing weak speech signals when a container is present and filtering out noise when no container is detected, resolving the contradiction between ease of operation and recognition accuracy.
3Measurement precision
If a start word is required to activate speech recognition, then speech recognition precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system dynamically changes the activation mode based on container presence. When a container is detected, the system activates speech recognition without requiring a start word, allowing users to give commands naturally. When no container is present, the system requires a start word to prevent misrecognition, thus adapting the activation requirements to the operational context.
4Measurement precision
If speech recognition sensitivity is decreased to prevent misrecognition, then speech recognition precision is improved, but ease of operation deteriorates
Solution Approach 1:
The speech recognition sensitivity is dynamically adjusted based on the detected container state. When a container is detected, the system operates in high-sensitivity mode to capture user commands even in noisy environments. When no container is present, the system switches to low-sensitivity mode to prevent misrecognition, thus adapting the recognition behavior to operational context.
Solution Approach 2:
The system changes the speech recognition sensitivity parameter according to the container presence state. This parameter adjustment allows the system to optimize between capturing weak speech signals when a container is present and filtering out noise when no container is detected, resolving the contradiction between ease of operation and recognition accuracy.
Data Source
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AI summary
Disclosed herein are an artificial intelligence apparatus and a method of operating the same. The artificial intelligence apparatus includes one or more processors that obtain weight data of a container and speech data, determines whether the container is seated on a seating portion of a water dispensing apparatus using the weight data, adjusts a speech recognition sensitivity according to whether the container is seated on the seating portion, inputs the first speech data to a speech recognition model and allows the water dispensing apparatus to perform a first water dispensing operation corresponding to first water dispensing information when the speech recognition model outputs the first water dispensing information based on the first speech data.