Voice Assistant Activation Detection Using Pause And Tone Analysis
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Solution Overview
Problem
Voice assistants often experience false activations due to unintentional triggers from designated words/phrases in different contexts and varying user enunciation, leading to misinterpretations and unintended activations, which detract from the user experience.
Innovation Solution
A computer-implemented method that analyzes initial utterances for predetermined wake-up words, evaluates the tone of subsequent utterances, and determines if the user pauses after speaking the wake-up words to identify genuine activation attempts, using AI-based models to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voice assistants use designated wake-up words for activation, then user interaction convenience is improved, but false activation rate increases
Solution Approach 1:
The system performs preliminary analysis of audio characteristics (tone, pitch, volume) before activation. By evaluating these features in advance, the system can filter out false activations while maintaining convenient wake-up word recognition, resolving the contradiction between ease of operation and reliability.
Solution Approach 2:
The system uses feedback from analyzing user responses and contextual information to refine activation detection. This continuous feedback loop allows the system to learn from actual usage patterns and reduce false activations while preserving user convenience.
2Measurement precision
If voice assistants analyze tone and pause patterns, then activation accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the audio analysis process into distinct components: wake-up word detection, tone analysis, pause pattern detection, and contextual verification. This segmentation allows complex accuracy improvements to be achieved through modular, manageable analysis stages rather than a single complex system.
Solution Approach 2:
The system applies partial analysis (tone and pause patterns) rather than complete analysis of all possible audio features. This partial action approach achieves sufficient accuracy improvement without the excessive complexity of analyzing every conceivable audio characteristic.
3Productivity
If voice assistants process all utterances, then user interaction responsiveness is improved, but processing time increases
Solution Approach 1:
The system extracts and processes only the most relevant audio features (tone, pause patterns, wake-up words) rather than processing all utterance data. This extraction approach maintains responsiveness by focusing computational resources on critical activation indicators while reducing overall processing time.
Data Source
AI summary
A computer-implemented method, according to one approach, includes: determining whether initial utterances received from a user include predetermined wake-up words. In response to determining that the initial utterances include the predetermined wake-up words, a voice assistant initiates an interaction with the user. In response to determining that the initial utterances in the audio signal stop for a predetermined period following the initial utterances that include the wake-up words, determining whether additional utterances are received from the user. In response to receiving additional utterances from the user, determining whether the additional utterances have an agitated tone. In situations where the additional utterances do not have an agitated tone, the additional utterances are processed. Furthermore, in response to determining that causing the additional utterances to be processed results in action being taken, the initial utterances are labeled as genuine dialog initiation entries.