An Iterative Approach to Discriminative Structure Learning

1. An Iterative Approach to Discriminative Structure Learning Peng Xu WS’2001

2. Discriminative Structure Learning Procedure • Baseline HMM training • Viterbi alignment of training data • Bivariate MI computation • Discriminate structure selection • Parameter re-estimation of new model WS’2001

3. Problems With the Procedure • Viterbi alignment may change after training the new model • MLE for parameter estimation • Viterbi approximation restricts the MI computation WS’2001

4. Discriminative Model Learning Goal: minimize D(P(Q|O)||P’(Q|O)) the divergence between the desired posterior probability distribution and the posterior probability distribution according to the model WS’2001

5. Desired Posterior Distribution Model Family Geometric Illustration of the Iterative Approach WS’2001

6. Iteration n Iteration n+1 BMM Structures WS’2001

7. EM Type Iterative Structure and Parameter Learning • E-step: Viterbi alignment of training data, MI computation • M-step: discriminative conditional mutual information based BMM edge detection, model parameter learning (MMI) WS’2001

8. Improving MI Computation • Viterbi alignment: • Label for each frame is deterministic • Posterior probability P(Qt=q|xt) is a  function • Soft alignment: • Compute P(Qt=q|xt) using forward-backward algorithm • Each data frame contributes to all labels WS’2001

9. Proposal for the Next Year • Formal formulation of the iterative model structure and parameter learning • Theoretical study of the EM type learning procedure • Implementation of the improved MI computation • Application to different data sets: Aurora, Audio-visual, etc. WS’2001