Residential Speaker Recognition via Context-Specific Background Models
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Solution Overview
Problem
Existing speaker recognition systems in residential environments face challenges due to background noise variability, lack of context-specific models, and privacy concerns with cloud-based technologies, as they often require uploading raw audio data.
Innovation Solution
A method and apparatus for residential speaker recognition that captures voice signals, extracts vocal features, and uses a locally adapted background model aggregated from similar contexts across multiple homes, ensuring user privacy by only sharing statistical representations of audio features, thus improving recognition accuracy and adaptability to changing environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a universal background model is used for speaker recognition, then the system can handle diverse speakers, but the recognition accuracy deteriorates in specific residential contexts with variable background noise
Solution Approach 1:
The patent segments the universal background model into multiple context-specific background models. Each model is trained on audio data from specific residential contexts (e.g., quiet room, noisy kitchen, outdoor patio). The system selects the appropriate segmented model based on the current environmental context, thereby maintaining both versatility across different speakers and accuracy within specific contexts.
Solution Approach 2:
The system dynamically switches between different background models based on real-time environmental sensing. When the context changes (e.g., from indoor to outdoor, or when background noise levels change), the system updates which background model is active, allowing it to adapt to varying residential environments while maintaining speaker recognition accuracy.
2Measurement precision
If cloud-based speaker recognition is used, then processing power and accuracy are improved, but user privacy deteriorates due to raw audio upload requirements
Solution Approach 1:
The system performs preliminary processing of audio data locally to extract speaker features and compute background models before any data leaves the device. This preliminary action occurs in the residential environment, ensuring that only processed feature data, not raw audio, is potentially shared. The background models are pre-computed and stored locally, eliminating the need to upload raw audio to the cloud.
Solution Approach 2:
The patent extracts only the necessary statistical representations of audio features from the raw audio data. Instead of uploading complete audio recordings to the cloud, the system extracts and shares only the processed feature vectors and background model parameters, which contain minimal privacy information while still enabling accurate speaker recognition.
3Measurement precision
If background models are trained in studio conditions, then the models are clean and accurate, but they fail to represent real-world residential acoustic conditions
Solution Approach 1:
The patent applies local quality by training separate background models for different local residential environments. Each model is trained on audio data collected from its specific location and context (e.g., home office, living room, outdoor area). This ensures that each model accurately represents the acoustic characteristics of its specific local environment while the system as a whole covers multiple residential scenarios.
Solution Approach 2:
The system creates copies of background models tailored to specific contexts rather than using a single generic model. Each context-specific background model is a copy trained on local audio data, allowing the system to replicate and adapt to the specific acoustic conditions of different residential spaces without sacrificing the accuracy provided by dedicated training data.
4Device complexity
If a single background model is used for all contexts, then the system is simple to implement, but it cannot adapt to changing environmental conditions over time
Solution Approach 1:
The system segments the background modeling function into multiple context-specific models organized in a database. Each segment corresponds to a specific environmental context and is selected based on current conditions. This segmentation maintains relative simplicity in individual model implementation while enabling complex adaptive behavior at the system level through context-aware model selection.
Solution Approach 2:
The system incorporates feedback from environmental sensors and usage patterns to determine which background model should be active. By continuously monitoring contextual information (time of day, location, detected activities) and using this feedback to select appropriate models, the system achieves adaptive behavior without requiring complex real-time model training or computation.
Data Source
AI summary
A home assistant device captures voice signal expressed by users in the home and extracts vocal features from these captured voice recordings. The device collects data about the current context in the home and requests from an aggregator a background model that is best adapted to the current context. This background model is obtained and locally used by the home assistant device to perform the speaker recognition. Home assistant devices from a plurality of homes contribute to the establishment of a database of background models by aggregating vocal features, clustering them according to the context and computing background models for the different contexts. These background models are then collected, clustered according to their contexts and aggregated by an aggregator in the database. Any home assistant device can then request from the aggregator the background model that fits best its current context, thus improving the speaker recognition.