Wake Word Selection Assistance Engine for False Detection Reduction
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Solution Overview
Problem
Selecting a suitable wake word for personal assistants is challenging due to the risk of false detections, as not all words or phrases are robust enough to avoid confusion with other sounds or words, leading to unnecessary activations.
Innovation Solution
The system provides assistance to users in selecting a robust wake word by using a wake word assistance engine that includes a pronunciation application to predict phonetic spelling, a uniqueness application to assess phonetic richness, and an accept application to determine false acceptance rates, allowing users to choose a wake word that minimizes false detections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a simple wake word selection process is used, then ease of operation is improved, but reliability deteriorates due to false detections
Solution Approach 1:
The system performs preliminary phonetic analysis, confusion checks, and false detection rate calculations before the user finalizes their wake word selection. The wake word assistance engine evaluates candidate wake words against a confusion dictionary and computes false detection rates in advance, allowing users to make informed selections without understanding complex phonetic concepts.
Solution Approach 2:
The wake word assistance engine acts as an intermediary between the user and the wake word recognition system. It translates user-selected wake words into phonetic representations, checks them against a confusion dictionary, and provides feedback on false detection rates, thereby mediating between simple user input and complex recognition reliability.
2Reliability
If expert knowledge is required for wake word selection, then reliability improves through better phonetic understanding, but ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically evaluating wake word candidates against phonetic criteria and false detection metrics. The wake word assistance engine independently computes confusion scores and false detection rates, eliminating the need for users to possess expert phonetic knowledge while ensuring reliable wake word selection.
Solution Approach 2:
The system provides feedback to users about the reliability of their selected wake words by displaying false detection rates and confusion scores. This feedback mechanism guides users toward selecting more robust wake words without requiring them to understand the underlying phonetic analysis.
3Measurement precision
If comprehensive phonetic analysis is performed, then measurement precision improves for wake word robustness, but device complexity increases
Solution Approach 1:
The wake word assistance engine is segmented into distinct functional modules: a phonetic spelling application that converts text to phonetic representations, a confusion dictionary that stores pre-computed confusion data, and a false detection rate calculator. This segmentation allows comprehensive phonetic analysis while managing system complexity through modular design.
Data Source
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AI summary
Generally discussed herein are devices, systems, and methods for custom wake word selection assistance. A method can include receiving, at a device, data indicating a custom wake word provided by a user, determining one or more characteristics of the custom wake word, determining that use of the custom wake word will cause more than a threshold rate of false detections based on the characteristics, rejecting the custom wake word as the wake word for accessing a personal assistant in response to determining that use of the custom wake word will cause more than a threshold rate of false detections, and setting the custom wake word as the wake word in response to determining that use of the custom wake word will not cause more than the threshold rate of false detections.