Real-time Verbal Harassment Detection in Vehicles
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Solution Overview
Problem
Current systems for detecting verbal harassment in ride-sharing vehicles rely on user reports, which are often delayed or not reported at all, due to the challenges of noisy audio data and the inefficiency of pattern matching, leading to ineffective real-time harassment identification.
Innovation Solution
A computer-implemented method using machine learning techniques, specifically neural network models, to predict harassment by converting audio segments into text and combining emotion detection, with models like hierarchical attention networks and convolutional neural networks, to determine harassment likelihood in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If user reports are used to detect harassment, then system complexity is low, but detection timeliness deteriorates (delayed or no reports)
Solution Approach 1:
The system performs preliminary action by continuously monitoring audio during the ride and automatically detecting harassment in real-time, rather than waiting for post-ride user reports. The machine learning models are pre-trained on harassment patterns to enable immediate detection and intervention.
Solution Approach 2:
The manual reporting mechanism (mechanical system relying on user action) is replaced with an automated machine learning-based detection system that processes audio data and identifies harassment patterns algorithmically, eliminating the need for user intervention.
2Measurement precision
If pattern matching is used for audio analysis, then processing speed is fast, but detection accuracy deteriorates (ineffective harassment identification)
Solution Approach 1:
Simple pattern matching (mechanical search approach) is replaced with machine learning models including hierarchical attention networks and convolutional neural networks that learn complex harassment patterns from training data, significantly improving detection accuracy while maintaining real-time processing capability.
Solution Approach 2:
The system changes the parameter of audio analysis from simple keyword pattern matching to sophisticated machine learning feature extraction and classification, transforming how harassment patterns are identified and improving both accuracy and robustness.
3Reliability
If simple detection methods are used, then device complexity is low, but detection reliability deteriorates (cannot handle noisy audio)
Solution Approach 1:
Simple detection methods are replaced with robust machine learning models that have been trained on noisy audio data, enabling reliable harassment detection even in challenging acoustic environments with background noise, music, or multiple speakers.
Solution Approach 2:
The system performs preliminary training of machine learning models on extensive datasets including noisy audio samples before deployment, preparing the models to handle real-world acoustic variations and improve reliability without requiring complex runtime processing.
Data Source
AI summary
In some cases, a verbal harassment detection system may use machine learning models to detect verbal harassment in real-time or near real-time. The system may receive an audio segment comprising a portion of audio captured by a microphone located within a vehicle. Further, the system may convert the audio segment to a text segment. The system may provide at least the text segment to a prediction model associated with verbal harassment detection to obtain a harassment prediction. Further, the system may provide the audio segment to an emotion detector to obtain a detected emotion of a speaking user that made an utterance included in the audio segment. Based at least in part on the harassment prediction and the detected emotion, the system may automatically, and without user intervention, determine whether a user is being harassed.