Wake Command Nullification via Audio Context Analysis
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Solution Overview
Problem
Computing devices with digital assistance and voice recognition capabilities often incorrectly identify audio content as valid wake commands, leading to unintended activation and frustration, especially when multiple devices are present in close proximity, with existing solutions failing to adequately address these issues.
Innovation Solution
A computer-implemented method and system that monitors audio content, maintains a running buffer, and determines the validity of detected wake word commands by analyzing context, silence duration, and conversational flow to reject invalid commands and prevent unnecessary device activation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If wake word detection is implemented to enable voice recognition, then digital assistance functionality is improved, but false activation from invalid audio content increases
Solution Approach 1:
The system performs preliminary actions by maintaining a running buffer of audio content and analyzing contextual patterns before final wake word validation. This allows the device to pre-process audio data and establish baseline characteristics of valid wake words, enabling more accurate distinction between intentional commands and false activations from background noise or conversational mentions.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring audio content and using the running buffer to provide contextual feedback about previous audio patterns. This feedback loop allows the wake word detection algorithm to adapt to environmental acoustic characteristics and user speaking patterns, improving reliability by distinguishing valid wake words from invalid audio content that may contain similar phonetic patterns.
2Adaptability or versatility
If multiple computing devices are deployed in close proximity, then system coverage and accessibility are improved, but cross-device command interference increases
Solution Approach 1:
The system applies segmentation by dividing the audio processing function into distributed components across multiple devices. Each device maintains its own running buffer and performs independent wake word detection on segmented portions of the audio environment. This segmentation, combined with device identification mechanisms, allows the system to scale coverage while reducing cross-device interference through decentralized processing.
Solution Approach 2:
The system implements local quality by optimizing wake word detection parameters and running buffer characteristics for each specific device's acoustic environment and position. Each device adapts its detection sensitivity and contextual analysis based on local conditions, allowing multiple devices to operate effectively in close proximity without uniform parameters causing widespread false activations.
3Measurement precision
If wake word detection sensitivity is increased to capture all valid commands, then command recognition accuracy is improved, but unintended activation from background noise increases
Solution Approach 1:
The system performs preliminary analysis of audio content in the running buffer to establish contextual patterns and acoustic characteristics before final wake word validation. This preliminary action allows the device to build a profile of legitimate wake word occurrences versus background noise patterns, enabling high sensitivity detection while maintaining low false activation rates through pre-established contextual understanding.
Solution Approach 2:
The running buffer acts as an intermediary between raw audio input and wake word detection decision. It provides a temporal and contextual buffer that mediates between high-sensitivity detection requirements and false activation prevention. The buffer allows the system to analyze patterns over time rather than making immediate decisions on individual audio samples, reducing unintended activation while maintaining command recognition accuracy.
Data Source
AI summary
A computing device includes a communication interface configured to interface and communicate with a communication system, an audio interface configured to interface and communicate with a user, a memory that stores operational instructions, and processing circuitry operably coupled to the communication interface, the audio interface, and to the memory that is configured to execute the operational instructions to perform various operations. The computing device monitors audio content, maintains a running buffer of most recent audio content, and detects a wake word command of the user. When detected, the computing device processes the most recent audio content including the wake word command of the user to determine validity/invalidity whether the wake word command of the user is invalid based on the most recent audio content. When invalid, the computing device rejects the wake word command of the user and continues to monitor the audio content and maintain the running buffer.