Speech Recognition Personality Detection via Linguistic Analysis
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Solution Overview
Problem
Current speech recognition systems struggle to accurately recognize and respond to user personality due to the inconclusiveness of pitch-based emotion detection, which relies on absolute values and fails to account for variations across demographics and age groups.
Innovation Solution
The system employs morphological and syntactic filters to analyze linguistic attributes such as word structure, word type, and parts of speech to determine user personality, using a weighting algorithm to assign scores based on extrovert or introvert traits, allowing for tailored dialog responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pitch-based emotion detection is used, then the system can detect user emotion, but the detection becomes inconclusive and inaccurate due to reliance on absolute pitch values
Solution Approach 1:
The patent changes the detection parameter from pitch (acoustic property) to linguistic attributes (morphological and syntactic properties of speech). By analyzing word structure, word type, and parts of speech instead of pitch values, the system achieves more reliable personality recognition that is not constrained by the inconclusiveness of pitch-based approaches.
Solution Approach 2:
The patent substitutes the mechanical/acoustic measurement system (pitch detection) with a linguistic analysis system. Instead of measuring physical sound characteristics, the system processes linguistic structure and composition to determine personality traits, thereby eliminating the limitations of acoustic-based emotion detection.
2Adaptability or versatility
If pitch characteristics are used to determine emotion, then the system can categorize emotions, but the categories become indeterminate and fail to account for demographic variations
Solution Approach 1:
The patent changes from using pitch characteristics to using linguistic attributes including morphological attributes (word structure, word type) and syntactic attributes (parts of speech). This parameter change enables the system to accurately measure personality traits across different demographics without the indeterminacy that plagues pitch-based approaches.
Solution Approach 2:
The patent segments the analysis into distinct linguistic components: morphological attributes and syntactic attributes. This segmentation allows for more precise measurement of personality characteristics by examining specific language usage patterns rather than relying on the indeterminate overall pitch characteristics.
3Measurement precision
If a weighting algorithm is applied to linguistic attributes, then personality traits can be determined with greater accuracy, but the system complexity increases
Solution Approach 1:
The patent applies a weighting algorithm to linguistic attributes (morphological and syntactic) to determine personality traits. By assigning weights to different linguistic features, the system achieves precise personality measurement while managing complexity through structured analysis of language patterns rather than requiring complex computational models.
Data Source
AI summary
Techniques are disclosed for recognizing user personality in accordance with a speech recognition system. For example, a technique for recognizing a personality trait associated with a user interacting with a speech recognition system includes the following steps/operations. One or more decoded spoken utterances of the user are obtained. The one or more decoded spoken utterances are generated by the speech recognition system. The one or more decoded spoken utterances are analyzed to determine one or more linguistic attributes (morphological and syntactic filters) that are associated with the one or more decoded spoken utterances. The personality trait associated with the user is then determined based on the analyzing step/operation.