Sentiment Analysis System Using Character-Level Audio Embeddings
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Solution Overview
Problem
Individuals are often unaware of the emotional state they convey through their speech and how it affects others, and there is a lack of feasible methods to continuously provide feedback on emotional states during interactions.
Innovation Solution
A system and method that processes characters from speech or text to determine sentiment data indicative of a user's emotional state, identifying non-users involved in interactions by comparing audio data with stored embedding vectors, and generating new vectors for unidentified individuals, allowing for context and identity determination without constant user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous monitoring of emotional states during interactions is implemented, then emotional awareness and feedback are improved, but device complexity and processing requirements increase
Solution Approach 1:
The system segments audio data into discrete character units from speech, processing them through separate neural network layers (embedding layer, convolutional layers, pooling layers) to determine emotional states. This segmentation allows complex emotional analysis to be broken down into manageable computational steps, improving measurement precision while controlling system complexity through modular processing stages.
Solution Approach 2:
The patent introduces character-level embeddings as an intermediary representation between raw audio data and emotional state determination. The embedding vectors serve as a mediator that transforms acoustic features into meaningful emotional indicators, reducing the complexity of direct emotional detection while maintaining high measurement precision through the intermediate processing layer.
2Measurement precision
If audio data from multiple non-users is processed to determine interactions, then identification accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of audio data into character embeddings and acoustic features before interaction analysis. By pre-computing these representations and storing them, the system reduces processing time during actual interaction detection while maintaining identification accuracy, as the heavy computational work is done in advance rather than in real-time during interactions.
Data Source
AI summary
Disclosed are systems and methods for determining sentiment data representative of an emotional state of a user during an interaction with one or more non-users. The disclosed implementations may further determine, for frequent interactions between the user and a non-user, the identity of the non-user and provide information to the user relating to the sentiment of the user when interacting with the non-user.