Speech Analysis Using Prosodic Segments for Personality Classification
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Solution Overview
Problem
Existing methods for determining behavioral and psychological characteristics from speech patterns are not situation-independent, as speech patterns are significantly affected by situational factors, making it difficult to measure personality traits consistently across varying contexts.
Innovation Solution
A computer-implemented method using secondary speech parameters such as rising-pitch, falling-pitch, and equal-pitch segments, which are less affected by individual differences, to analyze prosodic features and determine situational behavioral and psychological characteristics, enabling speaker-independent processing and identification of speech styles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech patterns are used to measure personality characteristics, then behavioral and psychological characteristics can be determined, but the measurement is affected by situational factors and lacks consistency across different contexts
Solution Approach 1:
The patent segments speech analysis into two distinct components: prosodic features (pitch, intensity, tempo, rhythm) and lexical/content features. By separating these elements, the system can focus on prosodic patterns that are more stable indicators of personality traits while minimizing the influence of situational factors that may affect speech content and style.
Solution Approach 2:
The patent transforms raw speech signals into specific prosodic parameters (fundamental frequency, intensity, duration, spectral features) that serve as stable indicators of personality characteristics. These parameter transformations enable consistent measurement across different situations by focusing on acoustic properties that reflect underlying personality traits rather than situational responses.
2Loss of information
If prosodic features are analyzed to determine emotional state, then emotional information can be extracted, but individual differences and speech style variations interfere with accurate classification
Solution Approach 1:
The patent extracts and isolates specific prosodic features (pitch contours, intensity variations, temporal patterns) from the overall speech signal, separating the emotional information carriers from individual speech style characteristics. This extraction process enables focus on universal emotional indicators while filtering out speaker-specific variations.
Solution Approach 2:
The patent develops a universal prosodic analysis framework that can identify emotional states across different speakers and languages. By focusing on fundamental prosodic patterns that are common to human speech rather than speaker-specific characteristics, the system achieves emotional recognition that works across diverse individuals and contexts.
3Reliability
If speech patterns are used to classify personality types, then behavioral characteristics can be identified, but the method requires sampling under very similar conditions to be reliable
Solution Approach 1:
The patent employs dynamic baseline adjustment that adapts to each speaker's natural speech patterns and the specific context of analysis. Rather than requiring fixed sampling conditions, the system dynamically calibrates to the speaker's prosodic characteristics and situational context, enabling reliable personality assessment across varying conditions through adaptive normalization.
Data Source
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AI summary
A computer implemented method of analyzing speech utterances of a speaker in a given situation and context and determining behavioral, psychological and speech style characteristics of the speaker in the given situation, said computer implemented method comprising: creating a speech parameters reference database for classifying speech utterances according to various behavioral, psychological and speech styles characteristics; obtaining speech utterances of a speaker in a specific situation and context; deriving a plurality of secondary speech parameters from said primary parameters; calculating a subset of speech parameters, parameters combinations and parameters' values representative of situational behavioral, psychological and speech styles characteristics, from said secondary parameters in the speech utterance; determining and scoring the situational behavioral, psychological and speech style characteristics in the speech utterance by comparing the calculated subset of speech parameters, parameters combinations and parameters' values with the pre-defined reference database of speech parameters.