A New Bigram-PLSA Language Model for Speech Recognition

1. A New Bigram-PLSA Language Model for Speech Recognition Mohammad Bahrani and HosseinSameti Department of Computer Engineering, Sharif University of Technology EURASIP 2010 報告者：郝柏翰

2. Outline • Introduction • Review of the PLSA Model • Combining Bigram and PLSA Models • Experiments • Conclusion

3. Review of the PLSA Model • Bag-of-words • Conditional independent

4. Combining Bigram and PLSA Models • Nie et al.’s Bigram-PLSA Model • Proposed Bigram-PLSA Model we relax the assumption of independence between the latent topicsand the context words and achieve a general form of the aspect model that considers the word history in the word document modeling.

5. Parameter Estimation Using the EM Algorithm • E-step

6. Parameter Estimation Using the EM Algorithm • M-step Let be the set of model parameters apply Bayes’ rule

7. Parameter Estimation Using the EM Algorithm • Using Jensen’s inequality

8. Jensen’s inequality

9. Parameter Estimation Using the EM Algorithm • appropriate Lagrange multipliers

10. Comparison with Nie et al.’s Bigram-PLSA Model. • The difference between our model and Nie et al.’s model is in the definition of the topic probability. • we relax the assumption of independence between the latent topics and the context words and achieve a general form of the aspect model that considers the word history in the word-document modeling. • The number of free parameters in our proposed model is in Nie et al.’s model is

11. Experiments

12. Experiments