Environment-Specific Voice Models for Noisy User Verification
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Solution Overview
Problem
User verification accuracy is degraded in noisy environments due to mismatches between enrollment and verification environments, leading to reduced performance and increased errors in authentication.
Innovation Solution
A device that performs context detection to select environment-specific user models and automatically generates new models based on user speech samples collected in various environments during normal use, without requiring additional enrollment operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If user verification is performed using a single enrollment model in noisy environments, then device complexity is reduced, but user verification accuracy deteriorates
Solution Approach 1:
The patent segments the single user model into multiple environment-specific user models. Each model is trained on speech samples collected from specific environments (e.g., quiet, noisy, reverberant). The system divides the verification task by selecting and applying the appropriate model based on detected environmental conditions, thereby maintaining verification accuracy across diverse settings without requiring a single overly complex universal model.
Solution Approach 2:
The patent implements dynamic model selection where the system adapts to changing environmental conditions by detecting the current environment and selecting the corresponding optimized user model. This dynamic approach allows the verification system to adjust its behavior based on real-time conditions, switching between different models as environments change, rather than using a static single model for all conditions.
2Measurement precision
If multiple environment-specific user models are maintained, then user verification accuracy in noisy environments is improved, but device complexity increases
Solution Approach 1:
The patent employs feedback mechanisms where the system continuously monitors environmental conditions during verification attempts. Based on this feedback, it selects the appropriate user model and can also identify when new environment types are encountered, triggering automatic collection of speech samples and generation of new environment-specific models. This feedback loop manages complexity by only creating new models when genuinely needed rather than pre-defining all possible environments.
Solution Approach 2:
The system performs self-service by automatically detecting new environments, collecting speech samples, and generating new user models without requiring manual re-enrollment from the user. The automatic model generation process handles the complexity of model management internally, reducing the burden on users while maintaining multiple environment-specific models.
3Adaptability or versatility
If automatic model generation is implemented, then adaptability to new environments is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by collecting speech samples in the background during normal device usage before formal verification is needed. When a new environment is detected, the system has already accumulated relevant speech data, allowing faster model generation compared to collecting samples on-demand. This preliminary data collection reduces the time penalty of automatic model generation.
Solution Approach 2:
The system manages computational resources by adjusting model generation parameters such as the number of speech samples required before triggering model creation, the complexity of the generation process, and the frequency of environment detection. These parameter adjustments balance adaptability with computational cost, generating models only when sufficiently confident about new environment conditions rather than continuously.
Data Source
AI summary
A device includes a memory configured to store multiple user models indicative of speech characteristics of a user. The device also includes one or more processors coupled to the memory and configured to obtain an audio input signal and perform a context detection operation to obtain environment information associated with the audio input signal. The processor(s) are configured to select a user model from among the multiple user models based on the environment information. The processor(s) are configured to obtain, based on the audio input signal and the selected user model, a user verification output indicative of whether the audio input signal corresponds to speech of the user. The processor(s) are configured to, based on obtaining a threshold number of samples of the user's speech in a particular environment, automatically generate a user model, of the multiple user models, indicative of the user's speech characteristics for the particular environment.