Voice Device Selection via Spatial Clustering
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Solution Overview
Problem
In environments with multiple voice-enabled devices, determining which device should respond to a voice command is challenging due to overlapping detection ranges, leading to confusion and inefficiency.
Innovation Solution
Generating a map of the user environment with designated regions for each device based on audio signal features and contextual information, using clustering algorithms to localize the user and select the appropriate device for response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If multiple voice-enabled devices are placed throughout the home to receive and respond to voice commands in different locations, then user accessibility and convenience are improved, but device selection complexity and response accuracy deteriorate
Solution Approach 1:
The system segments the home environment into multiple geographic zones, with each zone associated with a specific voice-enabled device. This segmentation allows the system to determine which device should respond to a voice command based on the user's current location, thereby maintaining ease of operation while reducing device selection complexity through spatial partitioning.
Solution Approach 2:
The system introduces a remote system as an intermediary that receives audio data from multiple voice-enabled devices, determines the user's location based on audio signal analysis, and selects the appropriate device to respond. This intermediary coordinates device selection centrally, reducing the complexity that would otherwise exist in the distributed device network.
2Area of stationary object
If multiple voice-enabled devices are deployed throughout the environment, then coverage area is improved, but determination accuracy of the intended device deteriorates
Solution Approach 1:
The system adds a spatial dimension to device selection by analyzing the geographic location of the user relative to multiple devices. Instead of relying solely on signal strength or device proximity metrics, the system uses audio data to determine which geographic zone the user occupies, thereby accurately identifying the intended device even in environments with extensive device coverage.
3Device complexity
If devices operate autonomously without centralized coordination, then system simplicity is improved, but response accuracy and user intent identification deteriorate
Solution Approach 1:
A remote system serves as a centralized intermediary that coordinates between multiple voice-enabled devices and the user. This intermediary receives audio data from devices, analyzes it to determine user location and intent, and selects the appropriate device for response. This centralized coordination improves user intent identification accuracy while maintaining relative system simplicity through a clear hierarchical structure.
Solution Approach 2:
The system implements feedback mechanisms where the remote system continuously monitors audio data from multiple devices, adjusts its understanding of user location and intent based on this feedback, and dynamically selects the most appropriate device for response. This feedback loop enhances accuracy without requiring complex autonomous decision-making at each device level.
Data Source
AI summary
This disclosure describes techniques for selecting which device to respond to user speech within an environment that includes multiple devices. To enable this selection, the techniques described herein create a model indicating a topology of the devices within the environment. To do so, features associated with the user speech are generated and used to generate multi-dimensional points, each representing a corresponding user utterance. The techniques may then group these multi-dimensional points into clusters before projecting the resulting clusters into two dimensions. The two-dimensional clusters may then be used to generate a model (e.g., a Voronoi diagram) representing regions of the environment, with the centroid of each region being associated with a respective device. When a user makes a subsequent request, the user may be localized within one of the regions and the device associated with that region may be chosen to respond to the user.