Speaker Recognition Using Local Density Models
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Solution Overview
Problem
Current speaker recognition systems face challenges in handling large, feature-rich data sets due to computational inefficiencies and inaccuracies, particularly in text-independent voice recognition, where significant data collection is required for reliable comparisons, and existing methods struggle with scalability and data distribution changes.
Innovation Solution
A voice recognition system that organizes speaker data into a high-dimensional data structure like a kd-tree for efficient approximate nearest neighbor searching, using kernel regression with Parzen windows to estimate a probability density function for identification, allowing for fast and accurate recognition without extensive modeling or parametric assumptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If parametric approaches (e.g., Gaussian Mixed Models) are used to reduce computations, then computational efficiency is improved, but accuracy deteriorates due to restrictive assumptions about data distribution
Solution Approach 1:
The patent changes the fundamental parameter of the approach from parametric (assuming specific distribution forms like Gaussian) to nonparametric (making no distribution assumptions). This allows the system to adapt to any data distribution while maintaining computational efficiency through local density estimation techniques that only require computing distances to nearest neighbors rather than fitting complex distribution models.
2Measurement precision
If discriminative approaches (e.g., support vector machines) are used to improve accuracy, then recognition accuracy is improved, but scalability deteriorates due to quadratic training complexity
Solution Approach 1:
The patent segments the global classification problem into local density estimation problems. Instead of training a single global classifier on all data points (which creates quadratic complexity), the system divides the feature space and estimates density locally around each test point using only its nearest neighbors. This segmentation reduces the computational burden from O(N²) to approximately O(N log N) or better depending on the data structure.
Solution Approach 2:
The patent applies local quality by estimating speaker density and making recognition decisions based on local characteristics rather than global models. Each test point is evaluated based on the density of training points in its local neighborhood, allowing the system to adapt to local data distributions without requiring a global model to scale efficiently.
3Measurement precision
If nonparametric approaches are used to avoid distribution assumptions, then accuracy is improved, but computation time deteriorates due to linear increase with training data size
Solution Approach 1:
The patent applies preliminary action by pre-organizing training data into efficient data structures (such as k-d trees or other spatial indexes) during the enrollment phase. This preliminary organization allows the system to quickly query for nearest neighbors during recognition without having to search through all training data linearly, reducing test time from O(N) to O(log N) or better while maintaining nonparametric accuracy.
Data Source
AI summary
A system and method for voice recognition is disclosed. The system enrolls speakers using an enrollment voice samples and identification information. An extraction module characterizes enrollment voice samples with high-dimensional feature vectors or speaker data points. A data structuring module organizes data points into a high-dimensional data structure, such as a kd-tree, in which similarity between data points dictates a distance, such as a Euclidean distance, a Minkowski distance, or a Manhattan distance. The system recognizes a speaker using an unidentified voice sample. A data querying module searches the data structure to generate a subset of approximate nearest neighbors based on an extracted high-dimensional feature vector. A data modeling module uses Parzen windows to estimate a probability density function representing how closely characteristics of the unidentified speaker match enrolled speakers, in real-time, without extensive training data or parametric assumptions about data distribution.