Speaker Recognition Scoring Using Diagonalized PLDA Inverses
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Solution Overview
Problem
Existing speaker recognition systems face challenges in efficiently computing and storing uncertainties of acoustic coverage, leading to high computational costs and memory usage, especially when dealing with short utterances, which affects the accuracy and efficiency of speaker identification and verification processes.
Innovation Solution
The method employs diagonalized full posterior probabilistic linear discriminant analysis (DFP-PLDA) to represent and compute uncertainties, discarding dependencies between sounds, thereby reducing computational costs and memory usage while maintaining high accuracy, by accumulating and transforming inverse uncertainties to produce diagonalized, transformed accumulated inverses for efficient scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full posterior PLDA is used to compute uncertainties of acoustic coverage, then measurement precision is improved, but device complexity and computational cost increase
Solution Approach 1:
The patent segments the full covariance matrix into diagonal components, treating each dimension independently. This diagonalization approach divides the complex uncertainty representation into simpler, independent parts that can be processed separately, reducing computational complexity while maintaining the essential uncertainty information needed for accurate speaker recognition scoring.
Solution Approach 2:
The patent extracts only the diagonal elements from the full covariance matrix, discarding the off-diagonal dependency terms. This extraction simplifies the uncertainty representation by keeping only the most critical variance information for each acoustic coverage dimension, significantly reducing memory usage and computational burden while preserving measurement precision.
2Measurement precision
If full covariance matrices are stored for uncertainty representation, then measurement precision is improved, but memory usage increases
Solution Approach 1:
The patent extracts only the diagonal elements from the full covariance matrices, storing only the variance terms rather than the complete covariance structure. This extraction reduces memory requirements from O(d²) to O(d) where d is the dimensionality, while maintaining the precision needed for uncertainty-aware speaker recognition scoring.
Solution Approach 2:
The patent applies local quality by treating each diagonal element independently with its own uncertainty value, rather than requiring global covariance information. This allows the system to maintain precise local uncertainty estimates for each acoustic coverage dimension without the memory burden of storing global covariance relationships between all dimensions.
3Measurement precision
If dependencies between uncertainties of different sounds are maintained, then measurement precision is improved, but computational cost and memory usage increase
Solution Approach 1:
The patent extracts and retains only the independent diagonal uncertainty components, deliberately discarding the off-diagonal dependency terms. This extraction enables efficient parallel processing of each sound's uncertainty independently, significantly improving processing efficiency while the diagonal uncertainty values continue to provide accurate measurement precision for speaker recognition.
Solution Approach 2:
The patent segments the uncertainty model into independent per-sound components by diagonalizing the covariance matrices. This segmentation allows each sound's uncertainty to be processed independently without requiring computation of joint dependencies, thereby improving productivity while maintaining sufficient measurement precision through the accumulated diagonal uncertainty representations.
Data Source
AI summary
A method for performing speaker recognition comprises: estimating respective uncertainties of acoustic coverage of respective speech utterance(s) by first and second speakers, the acoustic coverage representing respective sounds used by the speakers when speaking; representing the respective uncertainties of acoustic coverage in a manner that allows for efficient memory usage by discarding dependencies between uncertainties of different sounds for the speakers; representing the respective uncertainties of acoustic coverage in a manner that allows for efficient computation by representing an inverse of the respective uncertainties of acoustic coverage and then discarding the dependencies between the uncertainties of different sounds for the speakers; and computing a score between the speech utterance(s) by the speakers in a manner that leverages the respective uncertainties of the acoustic coverage during the comparison, the score being indicative of a likelihood that the speakers are the same speaker.