Context-Based Dynamic Wake Word Threshold for Virtual Assistants
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Solution Overview
Problem
Current speech recognition systems in virtual assistants often misinterpret user utterances, leading to accidental activation, which is a nuisance in various contexts such as home and office settings.
Innovation Solution
A context-based wake word threshold modification method that dynamically adjusts the activation threshold based on the user's context, using audio data processing and machine learning to differentiate between intended and unintended wake words.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed wake word threshold is used, then the virtual assistant can be activated reliably, but it causes accidental activations when misinterpreting user utterances
Solution Approach 1:
The patent applies dynamics by making the wake word threshold adjustable rather than fixed. The system dynamically modifies the threshold based on detected context factors such as user presence, ambient noise levels, and time of day. This allows the threshold to adapt to different situations, reducing accidental activations while maintaining reliable activation when needed.
Solution Approach 2:
The patent changes the parameter of the wake word threshold from a static value to a dynamic value that varies based on context. By monitoring contextual parameters (audio environment, user behavior patterns, device state) and adjusting the threshold accordingly, the system resolves the contradiction between reliable activation and preventing false positives.
2Productivity
If the wake word threshold is lowered to reduce missed activations, then more user commands are captured, but false activations increase
Solution Approach 1:
The system changes the threshold parameter dynamically based on contextual analysis. When the context suggests high likelihood of intentional activation (user presence detected, appropriate ambient conditions), the threshold is lowered to capture more commands. When context suggests low likelihood (no user presence, inappropriate environment), the threshold is raised to prevent false activations.
Solution Approach 2:
The wake word threshold becomes a dynamic parameter that adjusts in real-time based on contextual factors. This dynamic adjustment allows the system to optimize between capturing legitimate commands and preventing false activations, rather than using a single static threshold value.
3Object-affected harmful factors
If context-based dynamic threshold adjustment is implemented, then accidental activations are reduced, but system complexity increases
Solution Approach 1:
The patent segments the context analysis into distinct modular components: user presence detection, ambient noise analysis, time-based context, and behavior pattern recognition. Each component independently evaluates its aspect and contributes to the overall threshold adjustment decision, making the complex system more manageable and maintainable.
Solution Approach 2:
The system introduces a context analysis intermediary layer between audio input and wake word detection. This intermediary processes contextual information and generates a modified threshold value, acting as a mediator that simplifies the overall system architecture by separating concern between context understanding and speech recognition.
Data Source
AI summary
A method, computer system, and a computer program product for context-based wake word threshold modification is provided. The present invention may include receiving an audio data generated by a computing device operating in a sleep mode. The present invention may include processing the received audio data to determine a context associated with a user of the computing device. The present invention may include in response to detecting a potential wake word in the received audio data, calculating a wake word confidence score associated with an accuracy of the detected potential wake word. The present invention may include retrieving a wake word threshold, wherein the wake word threshold is determined based on the context associated with the user of the computing device. The present invention may include, in response to the calculated wake word confidence score exceeding the retrieved wake word threshold, activating a wake mode of the computing device.