Joint Probabilistic Linear Discriminant Analysis
Standard probabilistic linear discriminant analysis (PLDA) for speaker recognition assumes that the sample's features (usually, i-vectors) are given by a sum of three terms: a term that depends on the speaker identity, a term that models the within-speaker variability and is assumed independent across samples, and a final term that models any remaining variability and is also independent across samples. In this work, we propose a generalization of this model where the within-speaker variability is not necessarily assumed independent across samples but dependent on another discrete variable. This variable, which we call the channel variable as in the standard PLDA approach, could be, for example, a discrete category for the channel characteristics, the language spoken by the speaker, the type of speech in the sample (conversational, monologue, read), etc. The value of this variable is assumed to be known during training but not during testing. Scoring is performed, as in standard PLDA, by computing a likelihood ratio between the null hypothesis that the two sides of a trial belong to the same speaker versus the alternative hypothesis that the two sides belong to different speakers. The two likelihoods are computed by marginalizing over two hypothesis about the channels in both sides of a trial: that they are the same and that they are different. This way, we expect that the new model will be better at coping with same-channel versus different-channel trials than standard PLDA, since knowledge about the channel (or language, or speech style) is used during training and implicitly considered during scoring.
Code (0)
등록된 구현이 없습니다.
Tasks
Speaker RecognitionSimilar Papers 제목 키워드 기반
Joint Bayesian Gaussian discriminant analysis for speaker verification
State-of-the-art i-vector based speaker verification relies on variants of Probabilistic Linear Discriminant Analysis (PLDA) for discriminant analysis. We are mainly motivated by the recent work of the joint Bayesian (JB…
Face VerificationSpeaker VerificationSaliency-based Weighted Multi-label Linear Discriminant Analysis
In this paper, we propose a new variant of Linear Discriminant Analysis (LDA) to solve multi-label classification tasks. The proposed method is based on a probabilistic model for defining the weights of individual sample…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONLinear and Quadratic Discriminant Analysis: Tutorial
This tutorial explains Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) as two fundamental classification methods in statistical and probabilistic learning. We start with the optimization of d…
Dimensionality ReductionGeneral ClassificationMetric LearningregressionProbabilistic Deep Discriminant Analysis for Wind Blade Segmentation
Linear discriminant analysis improves class separability but struggles with non-linearly separable data. To overcome this, we introduce Deep Discriminant Analysis (DDA), which directly optimizes the Fisher criterion util…
Image SegmentationA State-Space Approach to Nonstationary Discriminant Analysis
Classical discriminant analysis assumes identically distributed training data, yet in many applications observations are collected over time and the class-conditional distributions drift. This population drift renders st…