Universal Context Aggregator for Speech Processing Latency
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Solution Overview
Problem
Current speech processing systems face delays in responding to user inputs due to the time-consuming process of obtaining and aggregating context information from various sources, which is essential for interpreting and executing user commands effectively.
Innovation Solution
A universal context aggregator is implemented to preconfigure a data framework of entities and their relationships, allowing it to proactively obtain context information likely to be used in processing user inputs, thereby reducing the time between input reception and action execution by aggregating context information quickly in response to queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If context information is obtained and aggregated from various sources in real-time, then the accuracy and completeness of speech processing is improved, but the response time increases causing delays
Solution Approach 1:
The system performs preliminary actions by proactively obtaining and caching context information before it is actually needed for speech processing. The context aggregator continuously gathers information from multiple sources (device state, application context, user profile, environmental sensors) and stores it in a context database, so that when a speech input occurs, the relevant context is already available and can be quickly retrieved rather than fetched in real-time.
Solution Approach 2:
The context aggregator acts as an intermediary component between the speech processing system and multiple context sources. It receives requests for context information, aggregates data from various sources (device state, applications, user profiles, sensors), and provides a unified context response. This intermediary layer decouples the speech processing from direct real-time queries to multiple sources, enabling faster response times while maintaining comprehensive context gathering.
2Speed
If context information is cached proactively, then the response time is reduced, but the device complexity increases due to additional components
Solution Approach 1:
The context aggregator is designed as a universal, multi-functional component that handles multiple types of context information (device state, application context, user profile, environmental data) through a single unified architecture. Rather than implementing separate caching mechanisms for each context source, the system uses one general-purpose context aggregator that can retrieve and cache various context types, thereby reducing overall system complexity while maintaining fast response capabilities.
Solution Approach 2:
The context aggregator implements self-service by automatically determining what context information to cache and when to update it, without requiring manual configuration or intervention. The system autonomously monitors context sources, identifies relevant information to cache, and manages the context database updates, reducing the operational complexity of maintaining the caching mechanism while achieving fast response times.
Data Source
AI summary
Systems including a universal context aggregator configured to pre-fetch context information that may be used to perform various processes with respect to a user input are described. The aggregator may have access to data representing what context information components of the system routinely request in various situations. When a particular situation is present, prior to being queried, the aggregator may pre-fetch context information that the aggregator is likely to be queried for.