Multi-Asynchronous Intent Recognition System
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Solution Overview
Problem
Conventional Speech Recognition systems face challenges in accurately determining intentions from speech due to issues like background noise, multi-source speech, and homonyms, which affect the precision of transcription and subsequent intent recognition, especially when relying on linear processes and limited knowledge bases.
Innovation Solution
A multi-asynchronous intention recognition system that employs text, phonetics, and audio classification modules to analyze speech data independently, using confidence weights to generate derived expressions of intent, and incorporates a discriminator module to process results from these modules, allowing for parallel processing and improved intent recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional Speech to Text systems use linear transcription processes with statistical models and built-in dictionaries, then the system structure remains simple and easy to implement, but the intent recognition precision deteriorates due to background noise, multi-source speech, and homonyms affecting transcription accuracy
Solution Approach 1:
The system segments the intent recognition process into three independent asynchronous modules: text classification module, phonetics classification module, and audio classification module. Each module processes speech data through different representations (text, phonetic, audio) and generates confidence-weighted intent representations. The discriminator module then integrates these segmented results to produce the final intent determination, resolving the contradiction by dividing complexity into manageable parallel components rather than a single complex linear process
Solution Approach 2:
The patent introduces a multi-dimensional analysis approach by processing speech data through three different representation dimensions simultaneously: text transcription, phonetic transcription, and audio signal processing. Each dimension provides independent insights and confidence scores, allowing the system to cross-validate results and improve intent recognition precision without relying on a single potentially flawed linear transcription pipeline
2Measurement precision
If the system uses custom-trained speech to text engines with domain-specific dictionaries and language models, then the transcription accuracy for specific domains improves, but the system adaptability to different domains and the prior STT problems remain limited
Solution Approach 1:
The system implements a universal intent recognition framework that can handle multiple domains simultaneously. The three classification modules (text, phonetics, audio) are designed to process any speech input through consistent multi-dimensional analysis, with the discriminator module adapting to different domains through confidence-weighted integration. This multi-functional architecture allows the same system structure to serve diverse domains without requiring separate specialized systems for each domain
Solution Approach 2:
The system dynamically adjusts the weighting parameters of different classification modules based on domain-specific characteristics and confidence scores. The discriminator module can modulate the influence of text, phonetic, and audio representations according to the specific domain requirements, allowing the system to adapt to different domains by changing parameter weights rather than requiring structural reconfiguration
3Reliability
If the system implements multiple asynchronous classification modules processing speech data in parallel, then the intent recognition accuracy and robustness improve, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary independent processing of speech data through three classification modules that operate simultaneously rather than sequentially. Each module processes a different representation (text, phonetic, audio) in parallel, preparing multiple intent representations and confidence scores before the final discrimination step. This preliminary parallel action reduces overall processing time compared to sequential processing while maintaining robustness through multi-dimensional analysis
Solution Approach 2:
The asynchronous architecture maintains continuous processing of speech data through overlapping operations. While the text classification module processes one segment, the phonetics and audio modules process other segments simultaneously, ensuring continuous useful action without idle waiting time. The discriminator module continuously integrates results from all three modules, maintaining steady progress toward final intent determination without the stop-start nature of sequential processing
Data Source
AI summary
A computerized system for deriving expression of intent from recorded speech includes: a text classification module comparing a transcription of recorded speech against a text classifier to generate a first set of representations of potential intents; a phonetics classification module comparing a phonetic transcription of the recorded speech against a phonetics classifier to generate a second set of representations; an audio classification module comparing an audio version of the recorded speech with an audio classifier to generate a third set of representations; and a discriminator module for receiving the first, second and third sets of the representations of potential intents and generating one derived expression of intent by processing the first, second and third sets together; where at least two of the text classification module, the phonetics classification module, and the audio classification module are asynchronous processes from one another.