Speech Prosody Extraction Engine for Linguistic Content Recognition
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Solution Overview
Problem
Conventional speech recognition systems fail to extract and utilize prosodic-linguistic patterns embedded in acoustic features, limiting their ability to recognize and interpret linguistic content effectively.
Innovation Solution
A method and engine for autonomous speech recognition that captures and analyzes speech continua to identify prosodic features such as speech rate deviations, pitch ratios, and intensity fluctuations, allowing for the extraction of linguistic content by segmenting speech into meaningful units and identifying discourse functions, sentiments, and socio-linguistic information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional speech recognition systems are used, then basic speech-to-text conversion is achieved, but prosodic-linguistic patterns embedded in acoustic features remain inaccessible
Solution Approach 1:
The speech signal is segmented into intonation segments based on prosodic features such as pitch contours, intensity patterns, and speech rate variations. This segmentation allows the system to isolate and analyze specific prosodic-linguistic patterns within the continuous speech stream, making previously inaccessible information available for processing.
Solution Approach 2:
The system extracts prosodic features (pitch, intensity, speech rate) from the acoustic signal and separates them from the basic phonetic content. By taking out these prosodic-linguistic patterns as distinct analytical targets, the system can process and interpret them independently while maintaining the overall speech recognition function.
2Loss of information
If speech is analyzed at basic phoneme level, then speech recognition functionality is maintained, but linguistic content embedded in prosody remains unrecognized
Solution Approach 1:
The speech recognition system is enhanced to perform multiple functions simultaneously: basic phoneme recognition and prosodic-linguistic pattern extraction. The same acoustic analysis infrastructure is used to serve both traditional speech-to-text conversion and the new function of identifying linguistic content embedded in prosodic features, achieving multi-functionality without requiring completely separate processing systems.
Solution Approach 2:
Prosodic features are calculated and analyzed in advance during the speech processing pipeline, before final linguistic interpretation. By performing preliminary extraction of pitch, intensity, and speech rate patterns, the system prepares prosodic-linguistic content for subsequent analysis, enabling automated recognition of emotional state, emphasis, and discourse structure.
3Measurement precision
If detailed prosodic analysis is performed, then linguistic content extraction is improved, but computational complexity increases
Solution Approach 1:
The system applies different levels of analytical precision to different aspects of prosodic analysis. Critical features such as pitch contours and intensity patterns receive detailed measurement, while less critical parameters use coarser analysis. This local differentiation of quality allows accurate extraction of linguistically important prosodic information while avoiding unnecessary computational overhead in areas where high precision is not required.
Data Source
AI summary
A prosodic speech recognition engine configured to identify prosodic features and patterns in a speech continuum for the extraction of linguistic content including para-syntactic content, discourse function, information structure, meaning, and speaker sentiment.