Low-Power Voice Command Detection Using Contextual Cues
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Solution Overview
Problem
Conventional voice response systems in mobile devices require manual or explicit triggers to engage, which can be inconvenient or prohibitive, especially when the device is in a low power mode, and they are unresponsive when inactive.
Innovation Solution
A method and system for mobile devices to monitor the acoustic environment to detect voice commands without explicit triggers, using a hierarchical processing approach with low-power initial stages and higher-power stages as needed, leveraging contextual cues and multiple processors to balance power consumption and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If conventional voice response systems require explicit trigger words or manual actions to engage, then power consumption is reduced when device is inactive, but user convenience deteriorates and hands-free access becomes prohibitive
Solution Approach 1:
The system performs preliminary voice command detection during low power mode before full device activation is required. The processor monitors for wake words or voice commands in a low-power state, and only fully activates when such commands are detected, thereby reducing unnecessary power consumption while maintaining hands-free accessibility.
Solution Approach 2:
The voice response system dynamically adjusts its operational state based on detected acoustic input. It transitions between low-power monitoring mode and full active mode depending on whether voice commands are detected, optimizing the balance between power consumption and user accessibility in real-time.
2Reliability
If voice response system monitors acoustic environment continuously in active mode, then responsiveness to voice commands improves, but power consumption increases
Solution Approach 1:
Instead of continuous monitoring, the system employs periodic acoustic environment sampling at reduced power levels. The processor periodically checks for voice commands or wake words rather than maintaining constant full-power monitoring, achieving acceptable responsiveness while significantly reducing power consumption during inactive periods.
Solution Approach 2:
The system performs preliminary low-power acoustic monitoring to detect potential voice commands before activating full processing resources. This preliminary detection stage operates at lower power consumption levels and only triggers full system activation when necessary, optimizing the trade-off between responsiveness and energy usage.
3Duration of action of stationary object
If device operates in low power mode, then battery life extends, but voice response system becomes unresponsive without multiple wake-up actions
Solution Approach 1:
The system performs preliminary voice command detection while in low power mode. The processor monitors for specific wake words or voice commands using reduced processing power, and when detected, immediately activates the full voice response system without requiring separate wake-up actions, thereby maintaining responsiveness while extending battery life.
Solution Approach 2:
The voice response system is designed to self-activate from low power mode upon detecting relevant acoustic input. Instead of requiring manual wake-up actions, the system automatically transitions from low-power state to active state when voice commands or wake words are detected, making the activation process seamless and maintaining ease of operation.
Data Source
AI summary
According to some aspects, a method of monitoring an acoustic environment of a mobile device, at least one computer readable medium encoded with instructions that, when executed, perform such a method and/or a mobile device configured to perform such a method is provided. The method comprises receiving acoustic input from the environment of the mobile device while the mobile device is operating in the low power mode, detecting whether the acoustic input includes a voice command based on performing a plurality of processing stages on the acoustic input, wherein at least one of the plurality of processing stages is performed while the mobile device is operating in the low power mode, and using at least one contextual cue to assist in detecting whether the acoustic input includes a voice command.