A plurality of corpora is received from one or more sources. A separate model is trained on each corpus of the plurality of corpora. The models for the plurality of corpora are merged into a joint model using parameter interpolation. The models for each corpus of the plurality of corpora are retrained separately using the joint model. A single model is created based on the retrained models.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising the steps of: receiving a plurality of corpora from one or more sources; training a separate model on each corpus of the plurality of corpora; merging the models for the plurality of corpora into a joint model using parameter interpolation; retraining the models separately for each corpus of the plurality of corpora using the joint model; and creating a single model based on the retrained models; wherein the steps are performed by at least one processor device coupled to a memory. 2. The method of claim 1, wherein the single model is a language model for use in a speech decoding process. 3. The method of claim 1, wherein training a separate model on each corpus comprises training exponential n-gram models. 4. The method of claim 1, wherein the training step comprises applying an Alternative Direction Method of Multipliers framework. 5. The method of claim 1, further comprising determining a log linear weight for each corpus of the plurality of corpora. 6. The method of claim 5, wherein merging the models comprises taking a weighted sum of a plurality of parameters across the plurality of corpora. 7. The method of claim 6, further comprising interpolating the plurality of parameters to create the joint model. 8. The method of claim 1, wherein retraining the models comprises using the joint model as a Gaussian prior. 9. The method of claim 1, wherein creating the single model comprises repeating the training, merging and retraining steps. 10. The method of claim 9, wherein the steps are repeated until convergence of a held-out perplexity. 11. An apparatus comprising: a memory and a processor operatively coupled to the memory and configured to implement the steps of: receiving a plurality of corpora from one or more sources; training a separate model on each corpus of the plurality of corpora; merging the models for the plurality of corpora into a joint model using parameter interpolation; retraining the models separately for each corpus of the plurality of corpora using the joint model; and creating a single model based on the retrained models. 12. The method of claim 11, wherein the single model is a language model for use in a speech decoding process. 13. The method of claim 11, wherein the training a separate model on each corpus comprises training exponential n-gram models. 14. The method of claim 11, wherein the training step comprises applying an Alternative Direction Method of Multipliers framework. 15. The method of claim 11, further comprising determining a log linear weight for each corpus of the plurality of corpora. 16. The method of claim 15, wherein merging the models comprises taking a weighted sum of a plurality of parameters across the plurality of corpora. 17. The method of claim 16, further comprising interpolating the plurality of parameters to create the joint model. 18. The method of claim 11, wherein retraining the models comprises using the joint model as a Gaussian prior. 19. The method of claim 11, wherein creating the single model comprises repeating the training, merging and retraining steps. 20. A computer program product comprising a computer readable storage medium for storing computer readable program code which, when executed, causes a computer to: receive a plurality of corpora from one or more sources; train a separate model on each corpus of the plurality of corpora; merge the models for the plurality of corpora into a joint model using parameter interpolation; retrain the models separately for each corpus of the plurality of corpora using the joint model; and create a single model based on the retrained models.