Classification and Ranking Approaches to Discriminative Language Modeling for ASR. Erinç Dikici , Murat Semerci , Murat Saraçlar , Ethem Alpaydın. IEEE TRANSACTIONS 2013. 報告者：郝柏翰. Outline. Introduction Methods Experiments Conclusion. Introduction.
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
Classification and Ranking Approaches toDiscriminative Language Modeling for ASR
Erinç Dikici, Murat Semerci, Murat Saraçlar, Ethem Alpaydın
the weights are averaged over the number of utterances (I)
and epochs (T) to increase model robustness:
Here the learning rates τ are hypothesis-specific, and are found by solving the following optimization problem:
Note that binary MIRA cannot be directly applied to our problem since we do not have the true labels of the instances; We propose single and multiple update versions of MIRA for structured prediction
If ra = 1 and rb = 2 then ra > rb