AI Speech and Text Analysis for Detecting Computer-Generated Calls
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Solution Overview
Problem
Existing technologies are inadequate in detecting and mitigating computer-generated fraudulent interactions, such as phishing attempts and unauthorized access, which can disrupt computer systems and compromise individual or organizational security.
Innovation Solution
An intelligent and automated speech and text recognition system using machine learning models to analyze voice calls and text chats, evaluating speech patterns, language usage, and interaction duration to determine the likelihood of computer-generated interactions, and generate alerts or commands to terminate suspicious activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fraud detection methods are used, then system complexity remains low, but detection precision is insufficient to identify computer-generated interactions
Solution Approach 1:
The system segments fraud detection into multiple independent analysis modules: audio analysis, text analysis, and interaction pattern analysis. Each module processes specific features (voice characteristics, language patterns, interaction timing) separately and contributes to the overall fraud score, enabling comprehensive detection while maintaining modular system architecture
Solution Approach 2:
The system introduces an intermediary AI analysis layer between the communication channels and the detection system. This intermediary layer transcribes audio to text, analyzes interaction patterns, and generates structured data that feeds into the fraud detection algorithm, bridging the gap between raw communications and detection capabilities
2Productivity
If manual monitoring of interactions is performed, then measurement precision is high, but productivity is low due to resource constraints
Solution Approach 1:
The system implements self-service through automated AI analysis that independently evaluates audio, text, and interaction patterns without human intervention. The automated fraud scoring system processes communications in real-time, generating fraud assessments that reduce manual review requirements while maintaining consistent detection accuracy across all interactions
Solution Approach 2:
The system replaces manual monitoring mechanisms with automated AI-based analysis. Machine learning models analyze voice characteristics, transcribe and evaluate text content, and assess interaction patterns, substituting human cognitive processes with computational algorithms that operate at higher speeds and scale to handle large volumes of communications
3Measurement precision
If comprehensive analysis of audio and text is performed, then detection precision improves, but use of energy increases
Solution Approach 1:
The system applies partial analysis by focusing on the most discriminative features for fraud detection. Rather than analyzing every aspect of audio and text equally, the system prioritizes key indicators such as voice synthesis characteristics, unnatural language patterns, and suspicious interaction sequences, achieving high detection precision while reducing computational energy requirements
Data Source
AI summary
Speech and text analysis processing for detecting computer-generated speech is provided. Speech and audio from a voice call or other interaction between two entities may be monitored and analyzed using one or more machine models. Audio may be transcribed and the resulting words and phrases used in the audio analyzed by a machine model to determine a likelihood that the audio is computer-generated. The audio may be separately analyzed to evaluate characteristics such as tones, inflections, accents, pitch, pace, and the like to determine a further likelihood of whether the audio is computer-generated. A duration of the audio may be used as a scoring factor. The various probabilities and scores may be combined to provide a composite score.