User-Specific Audio Feature Detection With Adversarial Perturbation
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Solution Overview
Problem
Existing audio feature detection systems in personal assistants face challenges with false negatives and false positives due to speech variations among users, such as accents and dialects, leading to incorrect recognition of audio features.
Innovation Solution
A user-specific perturbation generator model is trained using positive and negative audio samples to generate perturbations that improve the accuracy of audio feature detection by reducing false negatives and false positives, leveraging adversarial training techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional audio feature detection is used without user-specific adaptation, then the system is simple and fast, but it produces false positives and false negatives due to speech variations
Solution Approach 1:
The system performs preliminary adaptation by collecting user-specific audio samples during an enrollment phase and training a perturbation generator model before actual audio feature detection begins. This preliminary user-specific modeling enables the detection model to account for individual speech characteristics (accents, dialects, manner of speech) in advance, thereby reducing false positives and false negatives without adding complexity to the real-time detection process.
Solution Approach 2:
A perturbation generator model is introduced as an intermediary component between the audio input and the audio feature detection model. This intermediary generates user-specific perturbations that are applied to the audio samples before detection, effectively adapting the input data to the detection model's expectations for that specific user. This mediator layer enables accurate detection without requiring modifications to the core detection model architecture.
2Reliability
If the audio feature detection model is trained extensively to reduce false positives, then detection accuracy improves, but processing time and energy consumption increase
Solution Approach 1:
The system performs extensive training in advance during the enrollment phase, where user-specific perturbation generator models are trained on collected audio samples. This preliminary training consolidates the computational burden into an offline phase, allowing the actual audio feature detection to use lightweight perturbation application and standard detection processes that consume minimal energy in real-time operation.
Solution Approach 2:
Instead of training the main audio feature detection model extensively for each user, the system creates a lightweight perturbation generator model that copies and adapts user-specific characteristics. This copied adaptation mechanism allows the system to capture individual speech patterns without requiring repeated extensive training of the core detection model, thereby reducing processing energy consumption while maintaining reliability.
3Productivity
If the system responds to all detected audio features, then user responsiveness is high, but battery life is reduced due to unnecessary processing
Solution Approach 1:
The system replaces the traditional mechanical approach of always responding to detected audio features with an intelligent substitution using user-specific perturbation-based detection. By applying user-adapted perturbations, the system achieves more accurate detection that distinguishes true audio features from false positives, enabling selective response only when necessary. This substitution maintains high responsiveness for genuine user inputs while avoiding unnecessary processing and energy consumption for false detections.
Data Source
AI summary
Training a user-specific perturbation generator for an audio feature detection model includes receiving one or more positive audio samples of a user, each of the one or more positive audio samples including an audio feature; receiving one or more negative audio samples of the user, each of the one or more negative audio samples sharing an acoustic similarity with at least one of the one or more positive audio samples; and adversarially training a user-specific perturbation generator model to generate a user-specific perturbation, the training based on the one or more positive audio samples and the one or more negative audio samples. Perturbing audio samples of the user with the user-specific perturbation can cause an audio feature detection model to recognize the audio feature in audio samples that include the audio feature and/or to refrain from recognizing the audio feature in audio samples that do not include the audio feature.