Reinforcement Learning for Speech Processing Latency Optimization
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Solution Overview
Problem
Existing speech processing systems face challenges in optimizing performance, latency, and accuracy due to computationally expensive operations and varying processing rates among multiple natural language understanding processes, leading to inefficiencies in handling complex utterances and intent processing.
Innovation Solution
The implementation of reinforcement learning techniques to predict the output and processing time of each process, allowing for dynamic optimization of processing by recommending the termination or continuation of processes based on latency and accuracy constraints, and generating recommendations for intermediate results to improve overall system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple natural language understanding processes are used to handle complex utterances, then accuracy is improved, but processing time and computational cost increase
Solution Approach 1:
The system dynamically adjusts the number and type of NLU processes executed based on utterance complexity classification. Simple utterances use fast processes while complex utterances use accurate processes, making the system adaptive rather than static in its processing approach
Solution Approach 2:
The system changes the parameter of process selection (from always using all processes to selectively using processes based on complexity) to optimize the balance between accuracy and processing time for different types of utterances
2Measurement precision
If computationally expensive operations are performed to improve accuracy, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The system segments NLU processes into different complexity levels (fast processes and accurate processes) and assigns them based on utterance type, avoiding the application of expensive accurate processes to simple utterances where they are unnecessary
Solution Approach 2:
The system applies partial action by using only the necessary level of processing for each utterance - not all utterances receive the full computational treatment of multiple accurate processes, only those classified as complex do
3Reliability
If all natural language understanding processes are executed to ensure accurate intent processing, then reliability is improved, but device complexity increases
Solution Approach 1:
The system introduces dynamic process selection based on utterance complexity, replacing the static approach of always executing all processes. This dynamic adaptation maintains reliability for complex utterances while reducing unnecessary processing for simple ones
Data Source
AI summary
Devices and techniques are generally described for process optimization using reinforcement learning. In various examples, first input data is received and a first process for processing the first input data is determined. In some examples, a second process for processing the first input data is determined. A first machine learning model is used to generate a first prediction for processing the first input data by the first process. The first process and/or the second process are controlled based at least in part on the first prediction.