Speech Language Inference Model Self-Refinement
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Solution Overview
Problem
Existing language inference models generated through machine learning require large amounts of learning data to improve precision, but existing technologies do not provide a means to effectively address this need.
Innovation Solution
A processing system that acquires target speech data, infers the language using an inference model, determines the correctness of the inference result, and outputs the correct inference result as learning data to enhance the model, thereby increasing the learning data incrementally.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the amount of learning data is increased to improve inference precision, then language inference precision is improved, but data collection time and cost increase
Solution Approach 1:
The system implements a feedback mechanism where the determination unit verifies inference results and feeds back correct examples to the learning data storage unit. This creates a closed-loop system where the model continuously learns from verified correct inferences, improving precision without requiring extensive manual data collection. The feedback loop enables the system to self-improve by identifying and learning from its own successful predictions.
Solution Approach 2:
The system performs self-service by automatically generating learning data through its own operation. The learning data generation unit creates new learning examples from correctly inferred speech data, allowing the system to autonomously expand its learning dataset without external intervention. This self-service capability enables continuous model improvement while minimizing the need for manual data collection efforts.
2Measurement precision
If manual data collection is performed to increase learning data, then inference precision is improved, but operational complexity and cost increase
Solution Approach 1:
The system eliminates the need for manual data collection by implementing self-service mechanisms. The learning data generation unit automatically creates learning data from speech data and inference results, removing the burden of manual data collection operations. The system serves itself by generating the training data it needs through its own processing of speech inputs and inference outcomes.
Solution Approach 2:
The feedback mechanism automatically identifies correct inference results and converts them into learning data, replacing manual data collection operations. The determination unit's verification process feeds back to the learning data generation unit, creating an automated pipeline that eliminates the need for human operators to manually collect and prepare training data.
3Measurement precision
If existing inference models are used without refinement, then processing speed is maintained, but inference precision remains insufficient
Solution Approach 1:
The system adds a feedback-driven model refinement mechanism where determination results are fed back to update the inference model. This feedback loop enables the model to learn from correct inferences and continuously improve precision without requiring complex external refinement systems. The feedback mechanism transforms the model into a self-improving system that adapts to real-world speech data.
Solution Approach 2:
The inference model performs self-service by automatically generating its own refined training data through the learning data generation unit. The model uses its own inference results, verified by the determination unit, to create new learning examples, enabling self-refinement without external intervention. This self-service capability allows the model to improve precision while maintaining operational simplicity.
Data Source
AI summary
The example embodiments provides a processing system (10) including: an acquisition unit (11) that acquires target speech data in which a target speech is recorded or a target feature value that indicates a feature of the target speech; an inference unit (12) that infers a language of the target speech, based on an inference model for inferring a language of a speech from speech data or a speech feature value and the target speech data or the target feature value; a result output unit (13) that outputs an inference result by the inference unit (12); a determination unit (14) that determines whether the inference result is correct; and a learning data output unit (15) that outputs the inference result determined to be correct by the determination unit (14) and the target speech data or the target feature value, as learning data for generating the inference model.