Speech Processing With Multi-Modal Widget Context for Accurate NLU
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Solution Overview
Problem
Existing speech processing systems struggle to seamlessly integrate with multi-modal widgets on devices, limiting user interaction and multitasking capabilities, particularly in interpreting natural language inputs within the context of multiple active widgets.
Innovation Solution
A system that utilizes widget context data to interpret natural language inputs by determining the intent and entity data based on the active multi-modal widgets, selecting the appropriate skill component, and deciding how to present the output, whether inline or through a full-screen interface, enhancing user interaction and multitasking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If speech processing systems integrate with multi-modal widgets, then user interaction capability is improved, but system complexity increases
Solution Approach 1:
The speech processing system is designed to handle multiple types of inputs (voice commands, text inputs) and interact with various widget types (multi-modal widgets, traditional interfaces) through a unified processing architecture. The system uses a generic intent recognition framework that can interpret commands across different contexts without requiring separate processing pipelines for each widget type, thereby improving versatility while managing complexity.
Solution Approach 2:
The system introduces an intermediary layer consisting of intent recognition modules and context management components that mediate between the speech input and the various widget systems. This intermediary layer translates diverse user inputs into standardized intent representations, which then can be routed to appropriate widgets, simplifying the overall system architecture while enabling rich interactions.
2Measurement precision
If the system processes natural language inputs within the context of multiple active widgets, then interpretation accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-loading widget context information and maintaining an active context window that stores recent interactions and widget states before actual speech processing occurs. This preparation allows the intent recognition system to quickly access relevant context without performing heavy computations during the actual speech processing moment, thereby improving accuracy while minimizing processing time delays.
Solution Approach 2:
The system processes only the necessary portion of context information relevant to the current speech input rather than analyzing all possible widget contexts simultaneously. By using selective attention mechanisms that identify and process only the most relevant context elements, the system achieves high interpretation accuracy while reducing processing time compared to a brute-force analysis of all contexts.
Data Source
AI summary
Techniques for performing speech processing using multi-modal widget information are described. A system may receive input data corresponding to a user input. The system may also receive widget context data corresponding to one or more multi-modal widgets active at a device. The system may use the widget context data to perform natural language understanding (NLU) processing with respect to the user input, and for selecting a skill component for responding to the user input. The system may send a widget identifier to the skill component when invoking the skill to respond to the user input.