Semantic-Free Speech Graphs for Trait Prediction
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Solution Overview
Problem
Current speech analysis methods are limited as they only consider the semantic meaning of words, which can be misleading and fail to accurately identify traits or predict future states of individuals, as they do not account for non-semantic characteristics of speech patterns.
Innovation Solution
A method that collects and analyzes units of speech to create semantic-free speech graphs, matching their shapes to known categories to predict future states by identifying patterns independent of word meaning, using a system that populates nodes with tokens and identifies shapes in speech graphs to assign entities to categories and predict their future states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If speech analysis focuses on semantic meaning of words, then communication information evaluation is straightforward, but measurement precision of speech traits deteriorates
Solution Approach 1:
The patent extracts and removes semantic meaning from speech analysis by using semantic-free tokenization. Instead of analyzing what words mean, the system extracts only the structural form and sequence of speech units, creating speech graphs that represent temporal patterns without semantic content. This extraction of the essential structural feature while discarding semantic information resolves the contradiction by enabling precise trait identification through form alone.
Solution Approach 2:
The patent changes the analysis parameter from semantic meaning to structural form and temporal sequence. By transforming speech into speech graphs where nodes represent speech units and edges represent temporal relationships, the system shifts the measurement parameter from what is said (semantics) to how it is said (structure and timing), thereby improving measurement precision of speech traits.
2Device complexity
If speech analysis uses only word semantics, then analysis process is simple, but reliability of future state prediction deteriorates
Solution Approach 1:
The patent segments speech into discrete temporal units and represents them as nodes in speech graphs with edges indicating temporal sequence. This segmentation of speech into form-based units rather than semantic words allows the system to capture temporal patterns and structural relationships that are predictive of future states, thereby improving reliability without requiring complex semantic analysis.
Solution Approach 2:
The patent adds the temporal dimension to speech analysis by creating speech graphs that explicitly represent the sequence and timing of speech units. This dimensional transformation from static word semantics to dynamic temporal structures enables the system to capture evolutionary patterns in speech that improve prediction reliability for future states.
3Loss of information
If speech analysis considers semantic meaning, then information communication is clear, but accuracy of trait identification deteriorates
Solution Approach 1:
The patent extracts and isolates the structural-form information from speech while deliberately excluding semantic content. By using semantic-free tokenization and representing only the form and temporal sequence of speech units in speech graphs, the system achieves precise trait identification by analyzing how speech is structured rather than what it means, resolving the contradiction between information communication and trait identification precision.
Data Source
AI summary
A method, system, and/or computer program product uses speech traits of an entity to predict a future state of the entity. Units of speech are collected from a stream of speech that is generated by a first entity. Tokens from the stream of speech are identified, where each token identifies a particular unit of speech from the stream of speech, and where identification of the tokens is semantic-free. Nodes in a first speech graph are populated with the tokens, and a first shape of the first speech graph is identified. The first shape is matched to a second shape, where the second shape is of a second speech graph from a second entity in a known category. The first entity is assigned to the known category, and a future state of the first entity is predicted based on the first entity being assigned to the known category.