Wearable Audio Sentiment Analysis for Call Feedback
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Solution Overview
Problem
Individuals are often unaware of the emotional state they convey through their speech, which can affect others, and there is a need for real-time feedback to help them modify their behavior during conversations.
Innovation Solution
A system that detects speech during calls using wearable devices and processes audio data to determine the emotional state of the speaker, providing real-time or near-real-time sentiment analysis and feedback to the user, using techniques such as signal analysis and neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If real-time sentiment analysis is implemented during calls, then users become aware of their emotional tone and can adjust behavior, but device complexity and processing requirements increase
Solution Approach 1:
The patent introduces an intermediary sentiment analysis system that processes audio data separately from the main communication flow. The system captures audio during calls, analyzes emotional tone through neural networks, and provides feedback without interfering with the core communication function, thus adding capability while managing complexity
Solution Approach 2:
The patent replaces manual self-assessment of emotional state with automated computational analysis using neural networks and machine learning models. This substitution transforms the subjective, manual process into an objective, automated system that provides accurate real-time feedback
2Speed
If continuous audio processing is performed during calls, then real-time feedback is achieved, but energy consumption increases
Solution Approach 1:
The patent implements periodic sampling of audio data during calls rather than continuous processing. The system analyzes sentiment at intervals and provides periodic feedback, which maintains real-time awareness capability while significantly reducing computational load and energy consumption compared to continuous analysis
Solution Approach 2:
The system dynamically adjusts processing intensity based on call context and detected emotional states. When significant emotional changes are detected, analysis frequency increases to provide timely feedback, while during stable periods, processing reduces to conserve energy
3Loss of information
If speech detection and analysis is implemented, then users gain insight into their communication patterns, but privacy concerns arise from audio data collection
Solution Approach 1:
The patent extracts only the necessary emotional tone features from audio data while discarding personally identifiable information and specific content. The sentiment analysis focuses on acoustic characteristics like pitch, tone, and speech patterns rather than transcribing or storing actual speech content, thus providing insight while protecting privacy
Solution Approach 2:
The system creates abstract representations of emotional state from audio data rather than storing or transmitting the original audio recordings. Sentiment metrics and emotional patterns are captured as processed data, allowing analysis without retaining copies of sensitive personal audio content
Data Source
AI summary
Described are systems and methods that detect a call connection of a call (e.g., telephone call, social media call, etc.) between a first device of a user, such as a cellular phone, and a device of another person with which the user is to interact via the call. Upon detection of the call connection, another device of the user (e.g., wearable) records audio of the environment to detect speech output by the user during the call. The speech is then processed to determine sentiment(s) of the user during the call. The determined sentiment(s) of the user may then be presented to the user to aid the user in understanding how the speech of the user is perceived by others.