Spoken Request Interpretation Using Segmented Semantic Matching
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Solution Overview
Problem
Existing digital assistants face challenges in accurately and efficiently interpreting spoken requests due to ambiguities in human speech, leading to high computational costs and longer response latencies, especially when relying on natural language processing or rule-based algorithms.
Innovation Solution
A multi-stage processing technique is implemented, where audio input is first filtered through strict matching and rule-based conditions, and then processed using a machine-learned model to determine semantic equivalence, allowing for efficient execution of predefined task flows while ensuring accurate interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex processes are implemented to interpret spoken requests and address ambiguities, then interpretation accuracy is improved, but computational cost increases
Solution Approach 1:
The interpretation process is segmented into multiple stages: first checking for exact matches with invocation phrases, then applying rule-based conditions, and finally using machine-learned models only when necessary. This segmentation allows the system to achieve high accuracy while minimizing computational cost by using simpler methods for common cases.
Solution Approach 2:
The system performs preliminary checks using exact matching and rule-based conditions before invoking computationally expensive machine-learned models. This preliminary action filters out many cases that can be resolved with simpler methods, reducing overall computational cost while maintaining accuracy.
2Measurement precision
If complex processes are implemented to interpret spoken requests, then interpretation accuracy is improved, but response latency increases
Solution Approach 1:
The interpretation process is divided into sequential stages with increasing computational complexity. Exact matching and rule-based checks are performed first with minimal latency, and machine-learned model inference is reserved for cases that require it, thereby reducing average response latency while maintaining accuracy.
Solution Approach 2:
Preliminary filtering using exact matching and rule-based conditions is performed before invoking machine-learned models. This preliminary action quickly resolves many requests with minimal processing time, reducing overall response latency.
3Measurement precision
If complex processes are implemented to interpret spoken requests, then interpretation accuracy is improved, but power consumption increases
Solution Approach 1:
The system segments the interpretation workload so that low-power exact matching and rule-based processing handle the majority of cases, while high-power machine-learned model inference is used only when necessary. This segmentation significantly reduces average power consumption while maintaining interpretation accuracy.
Solution Approach 2:
Preliminary processing using low-power methods (exact matching, rule-based conditions) is performed before invoking high-power machine-learned models. This preliminary action reduces the frequency of high-power processing, thereby reducing overall power consumption.
4Adaptability or versatility
If natural language processing is used to interpret spoken requests, then handling of ambiguous speech is improved, but computational complexity increases
Solution Approach 1:
The system segments the processing complexity by applying simple exact matching and rule-based conditions first, then using machine-learned NLP models only for cases that require handling of ambiguity. This segmentation reduces average computational complexity while maintaining adaptability for ambiguous speech.
Solution Approach 2:
Preliminary processing using simple methods filters out many cases before NLP is invoked. This preliminary action reduces the frequency of complex NLP processing, thereby reducing overall computational complexity while maintaining the ability to handle ambiguous speech when needed.
Data Source
AI summary
In an exemplary process for interpreting spoken requests, audio input containing a user utterance is received. In accordance with a determination that a text representation of the user utterance does not exactly match any of a plurality of user-defined invocation phrases, the process determines whether a comparison between the text representation and a user-defined invocation phrase of the plurality of user-defined invocation phrases satisfies one or more rule-based conditions. In accordance with a determination that the comparison between the text representation and the user-defined invocation phrase satisfies the one or more rule-based conditions, the text representation and the user-defined invocation phrase is processed using a machine-learned model to determine a score representing a degree of semantic equivalence between the text representation and the user-defined invocation phrase. In accordance with a determination that the score satisfies a threshold condition, a predefined task corresponding to the user-defined invocation phrase is performed.