Voice Command Recognition Using Game Context Analysis
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Solution Overview
Problem
Current video game technologies struggle to accurately interpret voice commands in real-time, especially as game contexts change dynamically, leading to suboptimal gameplay experiences.
Innovation Solution
The method involves recording a player's speech during gameplay, analyzing the game state to identify the current context, and using a speech recognition model to convert the speech into textual content, which is then applied as gameplay input. If the recognition confidence is low, candidate words are presented to the player for selection, allowing for refinement of the speech recognition model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition is performed without context analysis, then processing speed is faster, but recognition accuracy deteriorates
Solution Approach 1:
The system performs preliminary analysis of the game state to identify context information (scene, location, avatar attributes, activity) before speech recognition occurs. This pre-prepared context data is then used to enhance the speech recognition model's ability to accurately interpret player commands, resolving the contradiction by preparing recognition aids in advance rather than adding complexity during the recognition moment.
Solution Approach 2:
The patent introduces context information as an intermediary element between the raw speech input and the recognition model. This context acts as a mediator that bridges the gap between unambiguous speech and accurate interpretation, providing the recognition system with contextual clues without requiring direct modification of the speech signal itself.
2Measurement precision
If real-time context analysis is performed, then voice command accuracy improves, but processing time increases
Solution Approach 1:
The game state analysis operates continuously throughout gameplay, constantly updating context information as the game progresses. This continuous operation allows the system to maintain current context data without requiring intensive batch processing, enabling real-time speech recognition to benefit from up-to-date context while minimizing processing interruptions and delays.
Solution Approach 2:
By continuously pre-computing and maintaining context information during gameplay, the system has recognition-ready data available when speech input occurs, eliminating the need for time-consuming analysis at the moment of recognition.
3Measurement precision
If speech recognition model is refined using player feedback, then recognition accuracy improves, but system complexity increases
Solution Approach 1:
The system implements a feedback mechanism where player corrections to misrecognized speech are captured and used to refine the speech recognition model. This feedback loop continuously improves recognition accuracy by learning from actual player intent, resolving the contradiction by systematically incorporating feedback rather than requiring complex manual model adjustments.
Solution Approach 2:
The speech recognition model performs self-improvement by automatically learning from player feedback without requiring external intervention or complex retraining procedures. The system serves itself by using gameplay data to continuously enhance its recognition capabilities, reducing the complexity burden on developers and players.
Data Source
AI summary
A method for executing a session of a video game is provided, including the following operations: recording speech of a player engaged in gameplay of the session of the video game; analyzing a game state generated by the execution of the session of the video game, wherein analyzing the game state identifies a context of the gameplay; analyzing the recorded speech using the identified context of the gameplay and a speech recognition model, to identify textual content of the recorded speech; applying the identified textual content as a gameplay input for the session of the video game.