Voice Input Visualization Interface for Neural Network Training
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Solution Overview
Problem
Voice-enabled systems fail to properly analyze natural language queries based on syntactic structure, leading to ineffective training of neural networks for responding to voice inputs.
Innovation Solution
A visualization interface that analyzes voice inputs by generating dependency tree structures, grouping them based on root words/phrases, and using these structures to train neural networks for predicting proper responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If voice enabled systems use traditional voice analysis methods, then the system is simpler to implement, but the system fails to properly analyze syntactic structure and determine proper responses
Solution Approach 1:
The patent segments the voice input analysis into distinct components: generating dependency tree structures to represent syntactic relationships, extracting features from these trees, and feeding them to a neural network. This segmentation allows the system to systematically break down complex syntactic analysis into manageable processing stages, improving analysis accuracy while maintaining implementability.
Solution Approach 2:
The patent introduces dependency tree structures as an intermediary representation between raw voice input and neural network processing. These trees serve as a mediating data structure that captures syntactic relationships in a structured format, enabling the neural network to effectively learn from syntactic patterns without requiring the system to directly implement complex syntactic analysis rules.
2Reliability
If voice enabled systems do not analyze syntactic structure, then the processing is faster and simpler, but the neural network cannot be effectively trained to provide proper responses
Solution Approach 1:
The patent applies preliminary action by generating dependency tree structures and extracting syntactic features before neural network training. This preprocessing step prepares the data in advance in a structured format that is optimized for learning, allowing the neural network to train more efficiently on syntactic patterns rather than learning from raw unstructured voice data.
Solution Approach 2:
The patent transforms the voice input data by changing its representation parameters from raw audio signals to structured dependency tree features. This parameter transformation converts unstructured voice data into a format with explicit syntactic relationships, enabling the neural network to learn more effectively from the transformed data with reduced training time.
3Measurement precision
If the system uses detailed dependency tree structures with multiple tags and nodes, then the syntactic analysis is more accurate, but the data processing complexity increases
Solution Approach 1:
The patent extracts specific relevant features from the detailed dependency tree structures rather than processing the entire tree. By selecting and extracting only the most pertinent syntactic features needed for response determination, the system maintains high analysis accuracy while reducing the complexity of data processing and neural network input requirements.
Data Source
AI summary
Voice inputs may be analyzed based on syntactic properties and respective dependency tree structure (e.g., parse tree) may be generated. Dependency tree structures may be grouped/clustered based on an associated root word/phrase. A visual interface may display the dependency tree structure in a manner that maps each voice input to a proper response (e.g., an action, an operation, a command, etc.). Mapping voice inputs to responses may be used to train a neural network,