Distilled Voice Assistant Models for Low-Latency Private Queries
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Solution Overview
Problem
Cloud-based voice assistant services face issues with increased latency, bandwidth consumption, and reduced privacy due to the need to transfer audio data from user devices to the cloud for processing.
Innovation Solution
A system that aggregates user queries, extracts attributes, and generates distilled models tailored for execution on client devices based on query patterns, allowing local processing of voice queries to improve latency and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud-based assistant service is used, then query processing capabilities are provided, but latency increases and privacy is reduced
Solution Approach 1:
The system segments the assistant service into two parts: a cloud-based teacher model that maintains full query processing capabilities, and distilled student models that are deployed to client devices for local inference. This segmentation allows complex queries to be processed in the cloud while simpler queries are handled locally, reducing latency without sacrificing overall processing capability.
Solution Approach 2:
The system creates simplified copies (distilled models) of the cloud-based assistant service that can execute locally on client devices. These student models are trained to replicate the functionality of the teacher model but with reduced complexity, enabling local processing that eliminates network latency while maintaining essential query processing capabilities.
2Productivity
If cloud-based assistant service is used, then query processing capabilities are provided, but network bandwidth is consumed
Solution Approach 1:
The system segments query handling between cloud and device, with distilled models processing local queries independently. This reduces the volume of data that needs to be transmitted over the network, as only queries that exceed the student model's capabilities are forwarded to the cloud-based teacher model.
Solution Approach 2:
By deploying local copies (distilled models) of the assistant service, the system eliminates the need to continuously transmit audio data and query results between cloud and device for routine operations, significantly reducing network bandwidth consumption while preserving query processing functionality.
3Productivity
If cloud-based assistant service is used, then query processing capabilities are provided, but user privacy is reduced
Solution Approach 1:
The system segments privacy-sensitive processing to occur locally on the user's device through distilled models. This ensures that personal queries and data remain on the device and are not transmitted to the cloud, while the cloud-based teacher model continues to provide comprehensive query processing capabilities for cases where local processing is insufficient.
Solution Approach 2:
By creating and deploying local copies (distilled models) of the assistant service, the system enables queries to be processed privately on the user's own device without requiring transmission to external cloud servers. This local execution capability preserves user privacy while maintaining query processing functionality through the student models.
Data Source
AI summary
A method includes receiving user queries directed toward a cloud-based assistant service. For each received user query directed toward the cloud-based assistant service, the method also includes extracting one or more attributes from the user query and logging the user query into one or more of a plurality of category buckets based on the one or more attributes extracted from the user query. The method also includes determining when at least one of the plurality of category buckets includes a threshold number of the user queries logged into the at least one category bucket, and when the at least one of the plurality of category buckets includes the threshold number of the user queries, generating a distilled model of the cloud-based assistant service. The distilled model of the cloud-based assistant service is configured to execute on one or more target client devices.