Personal Assistant Model Updates via User Intent Feedback
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Solution Overview
Problem
Existing personal assistant services using speech recognition and automatic translation technologies face difficulties in recognizing and translating user commands, especially in practical environments with different languages, due to limitations in model training and data availability, leading to degraded performance in recognizing proper nouns and unregistered words.
Innovation Solution
An apparatus and method that updates speech recognition, automatic interpretation, and automatic translation models based on user intentions and real-time data acquisition, including personalization and online data, to improve recognition and translation accuracy when interacting with users in foreign languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a basically trained model is used for speech recognition and automatic translation, then the system is simple to implement, but speech recognition of various user commands is difficult and reliability is degraded
Solution Approach 1:
The system performs preliminary actions by collecting user feedback and recognition results before finalizing the translation output. It pre-processes user corrections and stores them for model updates, improving reliability without requiring complex real-time processing during actual translation tasks.
Solution Approach 2:
The system implements feedback mechanisms where user corrections and recognition results are collected and used to update the translation model. This feedback loop continuously improves speech recognition reliability by learning from actual usage patterns and user preferences.
2Measurement precision
If a basically trained model is used for automatic translation, then the system requires minimal training data, but translation accuracy in practical environments with different languages is degraded
Solution Approach 1:
The system performs self-service by automatically collecting translation results and user feedback during normal operation, then using this data to update its own translation model. This self-learning mechanism improves translation accuracy without requiring extensive manual training data collection.
Solution Approach 2:
The system maintains continuous improvement by constantly collecting translation data and user feedback, then periodically updating the translation model. This continuous learning process ensures the system adapts to different languages and practical environments over time without requiring periodic retraining.
3Measurement precision
If the speech recognition model is updated based on user intentions, then recognition accuracy of user commands is improved, but processing time and system complexity increase
Solution Approach 1:
The system applies periodic action by updating the speech recognition model at scheduled intervals rather than continuously. It collects recognition data during operation, then performs model updates periodically, balancing improved command recognition accuracy with acceptable processing time and system resource usage.
Data Source
AI summary
Provided are an apparatus and method for providing a personal assistant service based on automatic translation. The apparatus for providing a personal assistant service based on automatic translation includes an input section configured to receive a command of a user, a memory in which a program for providing a personal assistant service according to the command of the user is stored, and a processor configured to execute the program. The processor updates at least one of a speech recognition model, an automatic interpretation model, and an automatic translation model on the basis of an intention of the command of the user using a recognition result of the command of the user and provides the personal assistant service on the basis of an automatic translation call.