Real-time Toxic Language Detection in Game Audio
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Solution Overview
Problem
Existing systems for managing toxic behavior in online games are cumbersome and time-consuming, often leading to users not reporting offensive language due to the manual effort required.
Innovation Solution
A system that processes audio data from online game conversations in real-time to detect, classify, and mitigate toxic language, using a deep neural network to identify inappropriate words and generate reports without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual reporting of offensive language is implemented, then toxic behavior can be recorded and addressed, but the process becomes cumbersome and time-consuming for users
Solution Approach 1:
The system performs automatic detection, classification, and reporting of toxic language without requiring user intervention. The language monitoring system independently identifies inappropriate content, determines its severity level, and generates reports to appropriate authorities, allowing the system to serve itself rather than relying on manual user reporting.
Solution Approach 2:
The system classifies and flags toxic language in real-time as it occurs during gameplay, before users would need to manually report it. By performing the detection and classification actions preliminarily and automatically, the system eliminates the need for users to go through cumbersome reporting procedures after the fact.
2Object-affected harmful factors
If real-time audio processing is implemented to filter toxic language, then harmful content is reduced before transmission, but system complexity increases
Solution Approach 1:
The language monitoring system is divided into distinct functional modules: audio capture, text-to-speech conversion, toxic language detection, severity classification, and selective filtering. Each module handles a specific aspect of the processing pipeline, making the overall complex system manageable through modular segmentation while maintaining real-time performance.
Solution Approach 2:
The system introduces an intermediary text representation layer between the audio input and the filtering decision. Audio is converted to text for analysis, allowing complex language understanding algorithms to operate on text data rather than raw audio, simplifying the detection process while maintaining accuracy in identifying toxic content.
3Extent of automation
If automatic classification system is deployed, then reporting burden on users is reduced, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by only processing and flagging audio segments that contain potentially toxic language, rather than analyzing every word uniformly. The severity classification system focuses computational resources on suspicious segments, performing detailed analysis only where needed while quickly passing through clean content, thus reducing overall processing time.
Data Source
AI summary
In various examples, game session audio data—e.g., representing speech of users participating in the game—may be monitored and/or analyzed to determine whether inappropriate language is being used. Where inappropriate language is identified, the portions of the audio corresponding to the inappropriate language may be edited or modified such that other users do not hear the inappropriate language. As a result, toxic behavior or language within instances of gameplay may be censored—thereby enhancing the user experience and making online gaming environments safer for more vulnerable populations. In some embodiments, the inappropriate language may be reported—e.g., automatically—to the game developer or game application host in order to suspend, ban, or otherwise manage users of the system that have a proclivity for toxic behavior.