Speech Routing Architecture Dynamic Adaptation
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Solution Overview
Problem
Current speech processing systems face challenges in accurately routing user requests to the most appropriate speech processing applications, often resulting in inefficient processing and potential misinterpretation of user intentions due to the lack of dynamic routing and contextual understanding.
Innovation Solution
A speech processing routing architecture that utilizes machine learning models trained with user feedback and contextual data to dynamically determine the best routing path for request data, incorporating a ranking and arbitration component that pre-computes features and adjusts routing dynamically to accommodate new services, ensuring accurate and safe processing of user requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional static routing methods are used to route user requests to speech processing applications, then the system structure is simple and easy to implement, but the routing accuracy and adaptability to new services deteriorate
Solution Approach 1:
The patent implements dynamic routing by training machine learning models with user feedback and contextual data to continuously adapt routing decisions. The system transitions from static routing rules to dynamic model-based routing that adjusts to new services and user preferences over time, improving routing accuracy while managing complexity through automated learning
Solution Approach 2:
The patent introduces a ranking and arbitration component as an intermediary between request routing and speech processing applications. This component pre-computes features and uses machine learning models to determine optimal routing paths, acting as a mediator that balances routing accuracy with system complexity by handling the computational burden centrally
2Adaptability or versatility
If dynamic routing with machine learning models is implemented, then the adaptability to new services and user feedback improves, but the computational resources and processing time required increase
Solution Approach 1:
The patent applies preliminary action by pre-computing features using contextual data and user feedback before actual request routing occurs. Machine learning models are trained in advance on historical data, and routing decisions are prepared beforehand through feature pre-computation, reducing real-time computational requirements while maintaining high adaptability
Solution Approach 2:
The system implements self-service through automated machine learning model training and updating using user feedback. The routing system continuously improves itself by learning from interactions without requiring manual reconfiguration, reducing the need for external computational resources while enhancing adaptability to new services
3Reliability
If comprehensive contextual data and user feedback are processed for routing decisions, then the understanding of user intentions improves, but the data processing time and system complexity increase
Solution Approach 1:
The patent reduces processing time by pre-computing contextual features and preparing routing candidates before actual user requests arrive. Historical contextual data and user feedback are processed in advance to train models and establish baseline routing patterns, enabling faster real-time decision-making while maintaining comprehensive understanding of user intentions
Solution Approach 2:
The system extracts and separates critical routing features from comprehensive contextual data through feature pre-computation. By identifying and extracting the most relevant features beforehand, the system reduces the volume of data that needs to be processed in real-time while retaining the essential information needed for accurate user intention understanding
Data Source
AI summary
Devices and techniques are generally described for a speech processing routing architecture. In various examples, first data comprising a first feature definition is received. The first feature definition may include a first indication of first source data and first instructions for generating feature data using the first source data. In various examples, the feature data may be generated according to the first feature definition. In some examples, a speech processing system may receive a first request to process a first utterance. The feature data may be retrieved from a non-transitory computer-readable memory. The speech processing system may determine a first skill for processing the first utterance based at least in part on the feature data.