Voice Fraud Detection Using Multi-Dimensional Speaker Profiles
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Solution Overview
Problem
Storing recordings of phone and video calls increases storage overhead and consumes power and processing resources, and existing machine learning models are ineffective in identifying customer-specific features for fraud detection.
Innovation Solution
A system using multi-dimensional voice analysis generates profiles for users based on pitch, tone, speaking rate, emotional state, and vocabulary, and applies a machine learning model to detect fraud by comparing audio streams to these profiles, enabling automated fraud prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If voice recordings are stored for fraud detection, then fraud detection capability is improved, but storage overhead and power consumption increase
Solution Approach 1:
The patent extracts only the essential voice quality features (pitch, tone, speaking rate, emotional state, vocabulary) from complete voice recordings and stores them as compact matrices. This extraction approach maintains fraud detection capability while dramatically reducing storage requirements and power consumption compared to storing full recordings.
Solution Approach 2:
The patent transforms voice recordings into different parameter representations (matrix formats representing voice quality dimensions). By changing the physical state from audio waveforms to compressed feature matrices, the system achieves efficient storage and processing while preserving the essential information needed for fraud detection.
2Measurement precision
If complete voice recordings are stored, then analysis accuracy is improved, but storage overhead increases
Solution Approach 1:
The system extracts only the critical voice quality parameters needed for fraud detection from complete recordings. By taking out only the essential features (pitch, tone, speaking rate, emotional state, vocabulary) and discarding redundant information, the patent achieves high analysis accuracy with minimal storage overhead.
Solution Approach 2:
The patent segments voice recordings into distinct feature dimensions (pitch, tone, speaking rate, emotional state, vocabulary) and represents each as a separate matrix. This segmentation allows efficient storage of only the necessary analytical components while maintaining the ability to perform comprehensive fraud detection analysis.
3Productivity
If existing machine learning models are used, then implementation speed is improved, but detection effectiveness deteriorates due to inability to identify customer-specific features
Solution Approach 1:
The patent implements local quality by creating customized voice quality profiles for each customer based on their specific speech characteristics. Instead of using a generic one-size-fits-all model, the system adapts to individual customers' pitch ranges, tone patterns, speaking rates, emotional baselines, and vocabulary, thereby improving detection effectiveness while maintaining implementation speed through efficient matrix comparisons.
Data Source
AI summary
In some implementations, a voice analysis system may receive, from a telecommunications system, an audio stream associated with a user. The voice analysis system may provide the audio stream to a machine learning model in order to receive a plurality of indicators associated with the audio stream. The plurality of indicators may be associated with a pitch of the user, a tone of the user, a speaking rate of the user, an emotional state of the user, or a vocabulary of the user. The voice analysis system may estimate whether the audio stream is associated with fraud based on the plurality of indicators. The voice analysis system may transmit, to an administrator device, an indication of whether the audio stream is associated with fraud.