Vocal Sentiment Analysis System for Emotional State Awareness
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Solution Overview
Problem
Individuals are often unaware of the emotional state they convey through their speech, which can impact others and their own well-being, making it difficult to adjust behavior without constant feedback from a friend or observer.
Innovation Solution
A system processes audio data from a user's speech to determine a session description indicative of emotional state, using neural networks and sentiment analysis to generate a summary of emotional prosody and sentiment descriptors, which can be presented to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a system processes audio data to determine emotional state, then self-awareness and behavior adjustment are improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent introduces an intermediary system that processes audio data and translates it into actionable emotional state information. The system acts as a mediator between the user's unconscious emotional expressions and their conscious self-awareness, providing feedback without requiring the user to directly analyze their own speech patterns. This intermediary approach resolves the contradiction by handling the complex processing internally while presenting simplified emotional insights to the user.
Solution Approach 2:
The patent replaces manual self-analysis of speech patterns with an automated computational system using neural networks and audio processing algorithms. Instead of mechanically requiring users to consciously monitor and analyze their own vocal emotions, the system substitutes this complex cognitive task with automated machine learning models that process audio data and generate emotional state descriptions, thereby reducing the perceived complexity for the end user.
2Measurement precision
If detailed sentiment analysis is performed on individual utterances, then measurement precision of emotional state is improved, but loss of time and processing overhead increase
Solution Approach 1:
The patent segments the audio data processing into distinct units of analysis, focusing on individual utterances rather than continuous streams. By dividing the speech into discrete utterance segments, the system can apply detailed sentiment analysis to each segment independently, maintaining high measurement precision while managing processing time through structured, modular analysis of smaller data units.
Solution Approach 2:
The patent applies sentiment analysis selectively to portions of speech that contain actionable emotional information, rather than uniformly analyzing all audio data. The system identifies and focuses processing on utterances that exhibit emotional prosody characteristics, performing detailed analysis only where necessary to maintain precision while reducing overall processing time by avoiding redundant analysis of non-emotional segments.
Data Source
AI summary
A device with a microphone acquires audio data of a user's speech. That speech comprises utterances, that together comprise a session. The audio data is processed to determine sentiment data indicative of perceived emotional content of the speech as conveyed by individual utterances of the user. That information is then used to determine the emotional content of the session. For example, the information may include several words describing the overall and outlying emotions of the session. Numeric metrics may also be determined, such as activation and valence. A user interface may present the words and metrics to the user. The user may use this information to assess their state of mind, facilitate interactions with others, and so forth.