Context-Aware Voice Command Recognition for Mobile Devices
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Solution Overview
Problem
Voice command recognition in mobile devices is inaccurate due to noise, unfamiliar accents, and untrained voices, and existing systems require a separate wake-up call for device activation.
Innovation Solution
A portable electronic communication device that combines user input options based on operational context, using a processor to interpret and execute user inputs without a prior trigger, incorporating sensors like cameras and AI engines to recognize voice and gesture inputs, and allowing context-specific triggers and commands for efficient device control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voice command recognition is used for device control, then hands-free operation is enabled, but recognition accuracy deteriorates due to noise, accents, and unfamiliar voices
Solution Approach 1:
The system segments voice recognition into two distinct phases: a wake-up phase that detects presence of the device owner through acoustic fingerprinting, and a command phase that processes actual commands. This segmentation allows each phase to be optimized independently, with the wake-up phase being more tolerant of acoustic variations while maintaining reliable device activation.
Solution Approach 2:
The system performs preliminary acoustic fingerprinting during the wake-up phase to establish the user's voice profile before processing actual commands. This preliminary action creates a reference model that improves subsequent command recognition accuracy by filtering out noise and accent variations in the command phase.
2Measurement precision
If separate wake-up call mechanism is used for device activation, then device control precision is improved, but user interaction complexity increases
Solution Approach 1:
The system merges the wake-up call mechanism with the voice command recognition system into a unified acoustic processing pipeline. The wake-up phrase detection is integrated with the command processing architecture, sharing the same acoustic fingerprinting and pattern recognition resources, thereby eliminating the need for separate activation mechanisms.
Solution Approach 2:
The acoustic processing system serves multiple functions: detecting the wake-up phrase, recognizing user presence through acoustic fingerprinting, and processing voice commands. This multi-functional approach eliminates the need for separate wake-up call mechanisms while maintaining precise device activation.
3Measurement precision
If contextual awareness is implemented for user input, then input accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary acoustic fingerprinting and user presence detection during the wake-up phase before processing actual commands. This preliminary action establishes the operational context and user profile in advance, allowing the command processing to proceed more quickly without needing to re-analyze acoustic characteristics.
Solution Approach 2:
The system dynamically adjusts the level of contextual analysis based on the operational state. When the device is in a ready state with user presence confirmed, it processes commands with full contextual awareness. When the device is transitioning states or user presence is uncertain, it uses faster, less computationally intensive processing methods.
Data Source
AI summary
Systems and methods for controlling a portable electronic communication device use device operational context to provide user trigger or command input. When user input is received from a user of the device, a set of user input options is selected based on an operational context of the device, including an identification of at least one running application. Each user input option is associated with a device action, and the received user input is mapped to a matching user input option within the selected set of user input options. The device action associated with the matching user input option is then executed.