Speech Recognition Routing in Provider Networks
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Solution Overview
Problem
The challenge lies in efficiently hosting automatic speech recognition (ASR) services on a single physical device, as the large file sizes of speech recognition engines, language models, and acoustic models lead to high network traffic, power usage, and latency, while also requiring granular control over routing to accommodate multiple ASR services and ensure data isolation for users.
Innovation Solution
Implementing an auto-scaled, distributed, multi-tenant hosting of machine learning models for ASR, using consistent hashing routing and load balancers to route ASR requests to appropriate cell groups with auto-scaling hosts, allowing for granular control and reducing the need for frequent swapping of large files, thereby minimizing network traffic and power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple ASR services are hosted on a single physical device, then device utilization is improved, but network traffic and power usage increase due to frequent swapping of large model files
Solution Approach 1:
The patent segments ASR services into separate cell groups, each hosted on dedicated physical devices. This segmentation prevents the need for frequent swapping of large model files on a single device, reducing power consumption associated with I/O operations and memory management, while still achieving high device utilization across the distributed infrastructure.
Solution Approach 2:
The patent transitions from a single-device vertical hosting model to a multi-device horizontal distributed model. By adding the dimension of spatial distribution across multiple physical devices, the system eliminates the need for frequent file swapping on single devices, thereby reducing power usage while maintaining or improving overall productivity.
2Productivity
If multiple ASR services are hosted on a single physical device, then device utilization is improved, but network traffic increases due to frequent swapping of large files
Solution Approach 1:
By segmenting ASR services into separate cell groups hosted on different physical devices, the system eliminates the need for frequent swapping of large model files that would occur on a single device. This segmentation reduces network traffic associated with file transfers and caching operations, while maintaining high device utilization across the distributed system.
3Reliability
If granular control over routing is implemented to accommodate multiple ASR services, then service isolation and data security are improved, but system complexity increases
Solution Approach 1:
The patent introduces a load balancer as an intermediary component that manages routing between client requests and cell groups. This intermediary abstracts the complexity of granular routing control, providing data isolation and service separation without requiring complex routing logic to be implemented throughout the entire system. The load balancer handles the complexity centrally, maintaining simplicity at the service level.
4Loss of energy
If ASR services are distributed across multiple physical devices, then power usage and network traffic are reduced, but latency may increase due to distributed architecture
Solution Approach 1:
The patent implements local quality by dedicating specific physical devices to specific cell groups, creating localized service environments. This localization reduces latency by minimizing the need for cross-device communication and file swapping, while still achieving energy efficiency through the distributed architecture. Each device is optimized for its specific workload, reducing unnecessary operations that would increase latency.
Data Source
AI summary
Techniques for speech recognition are described. For example, a speech recognition service is to receive a request to perform speech recognition on speech data from a chatbot using a particular speech recognition service; determine a group of hosts to route the speech data to, the group of hosts to host a plurality of speech recognition services including the particular speech recognition service; determine a path to the determined group of hosts using a set of one or more rules; determine a particular host of the group of hosts to perform speech recognition on the speech data, the particular host having the speech recognition service in memory to process the request and being preferred for performing the speech recognition on the speech data; route the speech data to the particular host; perform speech recognition on the speech data using the particular host; and provide a text result of the speech recognition.