Automatic Speech Recognition Model Selection for Peak Demand
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Solution Overview
Problem
Existing automatic speech recognition (ASR) systems face challenges in efficiently managing peak demand without over-provisioning resources, leading to wasted resources during non-peak times and high costs.
Innovation Solution
Implementing multiple ASR models with varying performance and capacity levels, dynamically switching between them based on demand, using criteria such as queue depth, acoustic attributes, and user behavior to optimize resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the ASR system provisions resources to support maximum peak demand, then the system can meet peak load requirements, but resources are wasted during non-peak times
Solution Approach 1:
The patent implements dynamic service level assignment that adjusts the ASR model selection based on current system state and demand conditions. The load supervisor continuously monitors queue depth and other metrics, dynamically switching between different service levels (high-accuracy models during low demand, lower-accuracy models during peak demand) to optimize resource utilization while maintaining reliability when needed
Solution Approach 2:
The system changes operational parameters by selecting different ASR models with varying accuracy and resource consumption characteristics based on current demand. During peak periods, the system transitions to models with fewer computing resources; during off-peak periods, it uses higher-accuracy models, thereby adapting resource usage to actual needs and eliminating waste
2Productivity
If multiple ASR models with different accuracy levels are used, then resource utilization is optimized, but system complexity increases
Solution Approach 1:
The patent segments the ASR service into multiple distinct models with different accuracy and resource consumption characteristics. Each model is optimized for specific demand conditions, allowing the system to select the appropriate segment (model) based on current needs. This segmentation enables efficient resource utilization while keeping each individual model relatively simple
Solution Approach 2:
The load supervisor acts as an intermediary that manages the complexity of selecting among multiple ASR models. It monitors system state, determines appropriate service levels, and routes requests to suitable models, thereby shielding the rest of the system from the complexity of managing multiple models while still achieving optimized resource utilization
Data Source
AI summary
A method for automatic speech recognition (ASR) of audio data streams involves obtaining a service level indication to determine the appropriate ASR model from a set of models. The set includes at least a first model with higher accuracy and greater computing resource requirements, and a second model with lower accuracy and reduced resource demands. The method includes selecting an ASR model based on the service level indication, receiving the audio data stream, and executing ASR using the chosen model. This approach allows for dynamic adaptation of ASR processing based on available resources and desired accuracy, optimizing performance and resource allocation.