Joint Factor Analysis for Speaker Recognition Reinterpreted as Signal Coding Using Overcomplete Dictionaries

This paper presents a reinterpretation of Joint Factor Analysis as a signal approximation methodology―based on ridge regression―using an overcomplete dictionary learned from data. A non-probabilistic perspective of the three fundamental steps in the JFA paradigm based on point estimates is provided. That is, model training, hyperparameter estimation and scoring stages are equated to signal coding, dictionary learning and similarity computation respectively. Establishing a connection between these two well-researched areas opens the doors for cross-pollination between both fields. As an example of this, we propose two novel ideas that arise naturally form the non-probabilistic perspective and result in faster hyperparameter estimation and improved scoring. Specifically, the proposed technique for hyperparameter estimation avoids the need to use explicit matrix inversions in the M-step of the ML estimation. This allows the use of faster techniques such as Gauss-Seidel or Cholesky factorizations for the computation of the posterior means of the factors x, y and z during the E-step. Regarding the scoring, a similarity measure based on a normalized inner product is proposed and shown to outperform the state-of-the-art linear scoring approach commonly used in JFA. Experimental validation of these two novel techniques is presented using closed-set identification and speaker verification experiments over the Switchboard database.