Wakeup Indicator Monitoring Using Audio Confidence Analysis
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Solution Overview
Problem
Existing methods for monitoring the wakeup indicator of intelligent voice interaction devices are inefficient and require manual annotation, leading to inaccuracies and high resource consumption.
Innovation Solution
A method and apparatus for monitoring wakeup indicators using automated audio data analysis, determining wakeup confidence and calculating wakeup rates or false wakeup rates through machine learning models, enabling unsupervised and scalable monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to monitor wakeup indicators, then measurement precision can be maintained, but productivity is reduced and resource consumption increases
Solution Approach 1:
The patent replaces manual annotation (mechanical human operation) with automated audio data analysis using machine learning models. The system automatically processes audio data, determines wakeup confidence scores, and calculates wakeup rates without human intervention, thereby maintaining measurement precision while dramatically improving productivity.
Solution Approach 2:
The system enables self-service monitoring by automatically analyzing audio data and generating wakeup indicator metrics without requiring manual annotation. The machine learning model autonomously processes the audio data, determines whether wakeup words are present, and calculates the wakeup rate, making the monitoring process self-sufficient.
2Measurement precision
If manual annotation is used to monitor wakeup indicators, then measurement precision can be maintained, but loss of time increases
Solution Approach 1:
The patent replaces time-consuming manual annotation with automated machine learning-based analysis. The system rapidly processes audio data through trained models to determine wakeup confidence and calculate metrics, eliminating the time loss associated with manual human annotation while preserving measurement precision.
Solution Approach 2:
The system performs preliminary action by pre-training machine learning models on annotated data beforehand. Once trained, the models can automatically and quickly analyze new audio data without requiring real-time manual annotation, thus reducing time loss during actual monitoring operations while maintaining precision through the pre-established model accuracy.
3Productivity
If automated audio data analysis is used, then productivity is improved and time consumption is reduced, but device complexity increases
Solution Approach 1:
The patent introduces automated machine learning models to replace manual processes, which does increase system complexity by adding computational components. However, this complexity is justified and managed by the significant gains in productivity and the use of standardized ML frameworks that make the complex components modular and maintainable.
4Productivity
If automated audio data analysis is used, then productivity is improved, but use of energy increases
Solution Approach 1:
The patent replaces manual annotation with automated machine learning analysis, which increases energy consumption due to computational processing. However, this energy trade-off is acceptable because the automation eliminates the need for human annotators and enables scalable processing of large volumes of audio data, resulting in net productivity gains that justify the energy investment.
Data Source
AI summary
The present wakeup indicator monitoring method, apparatus and electronic device includes: acquiring M pieces of audio data of a device to be monitored; determining a first wakeup confidence of each piece of M pieces of audio data, wherein the first wakeup confidence indicates a probability that the audio data contains a first wakeup word for waking up the device to be monitored; acquiring a first audio data with a first wakeup confidence in a target zone in M pieces of audio data, wherein the wakeup confidence in the target zone indicates that the audio data contains a wakeup word for waking up an audio device; and determining the ratio of the first audio data to M pieces of audio data as a wakeup rate of a device to be monitored, where the wakeup indicator of the device to be monitored includes the wakeup rate.