Situational Context Inference for Accurate Speech Interpretation
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Solution Overview
Problem
Existing speech recognition systems struggle to accurately interpret user inputs and provide appropriate responses due to a lack of contextual awareness, leading to suboptimal user experiences.
Innovation Solution
A virtual assistant system that generates situational context data by combining environmental signals and prior knowledge to enhance interpretation and response accuracy, using a situational context data inference component (SCIC) to process source signals and prior knowledge data to generate natural language descriptions of the user's environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition systems operate without contextual awareness, then system complexity is reduced, but interpretation accuracy deteriorates
Solution Approach 1:
The system performs preliminary actions by gathering and processing environmental signals and prior knowledge before the user speaks. The situational context data inference component continuously monitors environmental signals (location, weather, time, device state) and combines them with prior knowledge to generate situational context data that is ready to be applied when interpreting user speech, thereby improving interpretation accuracy without adding complexity during the actual speech recognition moment
Solution Approach 2:
The patent introduces an intermediary component called the 'situational context data inference component' that mediates between raw environmental signals and speech interpretation. This intermediary gathers environmental signals, combines them with prior knowledge, and generates situational context data that enhances speech recognition accuracy without requiring the entire system to become significantly more complex
2Adaptability or versatility
If the system processes more environmental signals and prior knowledge to generate situational context data, then response relevance is improved, but processing time increases
Solution Approach 1:
The system performs preliminary processing of environmental signals and generation of situational context data in advance, before the user speaks. By continuously monitoring and pre-processing environmental signals (location, weather, time, device state) and combining them with prior knowledge, the system prepares contextual information that can be quickly applied during speech recognition, reducing real-time processing delays while maintaining high response relevance
Solution Approach 2:
The situational context data inference component operates continuously, constantly monitoring environmental signals and updating situational context data without interruption. This continuous operation ensures that when speech recognition occurs, relevant contextual information is already ready, eliminating the need for time-consuming on-demand processing while maintaining adaptability and response relevance
Data Source
AI summary
A system may be configured to receive and process various signals to generate a natural language description of a user's environment, called situational context data. The signals may include sensor data, device status, user activity, user input, and/or inferences made using such data. The situational context data may express a user-centric description of the user's environment; for example: “User is taking a walk in the park on a sunny afternoon” or “activity: driving location: highway”, etc. The system may send the situational context data to various system components that may, for example, process speech, select applications/skills for handling user inputs, and/or that implement those applications/skills. The applications/skills may use the situational context data to provide recommendations, generate responses, and/or perform actions that are more relevant to the user's current environment.