Adaptive Speech Meaning Learning via Restatement Detection
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Solution Overview
Problem
Existing speech recognition systems fail to adaptively learn the meaning of a first speech based on whether a second speech is a restatement of the first speech, leading to degraded learning quality and inconsistent application of learning results across users and contexts.
Innovation Solution
An information processing apparatus and method that includes a learning unit capable of determining if a second speech is a restatement of a first speech, allowing the learning unit to adaptively learn the meaning of the first speech recognition result based on this determination, and apply it appropriately to users and contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition systems process speeches without adaptive learning based on restatement determination, then the system structure remains simple, but the learning quality and recognition accuracy are degraded
Solution Approach 1:
The system segments the speech processing into distinct functional modules: a determination unit that identifies whether a second speech is a restatement of a first speech, and a learning unit that adaptively learns meanings based on these determination results. This segmentation allows the system to add intelligence without creating a monolithic complex structure.
Solution Approach 2:
The determination unit performs preliminary analysis to identify restatement relationships before the learning unit processes the speech data. By pre-identifying which speeches are restatements, the system prepares structured information that guides the subsequent learning process, improving efficiency and accuracy.
2Reliability
If the system learns speech meanings adaptively based on restatement determination, then learning quality improves, but the processing time and computational resources increase
Solution Approach 1:
The learning unit automatically adjusts its learning strategy based on the determination results without requiring external intervention. When restatements are detected, the system self-adapts by prioritizing learning from these paired speech examples, improving learning quality while maintaining efficient automated operation.
Solution Approach 2:
The system dynamically changes learning parameters based on the restatement determination results. When restatements are identified, the learning unit modifies its training focus and data weighting, allowing adaptive learning quality improvement without requiring complete reprocessing of all speech data.
3Adaptability or versatility
If the system applies learning results universally without context consideration, then the application scope is wide, but the relevance and appropriateness of learning application are reduced
Solution Approach 1:
The system applies learning results selectively based on local context characteristics. The determination unit identifies specific contexts where restatement relationships exist, and the learning unit applies learned meanings primarily to these identified contexts, ensuring high relevance while maintaining the ability to generalize when appropriate.
Data Source
AI summary
[Problem] There are proposed an information processing apparatus, an information processing method, and a program, which are capable of learning a meaning corresponding to a speech recognition result of a first speech adaptively to a determination result as to whether or not a second speech is a restatement of the first speech.[Solution] An information processing apparatus including: a learning unit configured to learn, based on a determination result as to whether or not a second speech collected at second timing after first timing is a restatement of a first speech collected at the first timing, a meaning corresponding to a speech recognition result of the first speech.