Structured Language Modeling Forspeech

We present revised Wall Street Journal (WSJ) lattice rescoring experiments using the structured language model (SLM). 1 Experiments We repeated the WSJ lattice rescoring experiments reported in 1] in a standard setup. We chose to work on the DARPA'93 evaluation HUB1 test set | 213 utterances, 3446 words. The 20kwds open vocabulary and baseline 3-gram model are the standard ones provided by NIST. As a rst step we evaluated the perplexity performance of the SLM relative to that of a deleted interpolation 3-gram model trained under the same conditions: training data size 20Mwds (a subset of the training data used for the baseline 3-gram model), standard HUB1 open vocabulary of size 20kwds; both the training data and the vocabulary were re-tokenized such that they conform to the Upenn Treebank tokenization. We have linearly interpolated the SLM with the above 3-gram model: P () = P 3gram () + (1 ?) P SLM () showing a 10% relative reduction over the perplexity of the 3-gram model. The results are presented in Table 1. The SLM parameter reestimation procedure 1 reduces the PPL by 5% (2% after interpolation with the 3-gram model). The main reduction in PPL comes however from the interpolation with the 3-gram model showing that although overlapping, the two models successfully complement each other. The interpolation weight was determined on a held-out set to be = 0:4. Both language models operate in the UPenn Treebank text tokenization. A second batch of experiments evaluated the performance of the SLM for 3-gram 2 lattice decoding. The lattices were generated using the standard baseline 3-gram language model y 1 Due to the fact that the parameter reestimation procedure for the SLM is computationally expensive we ran only a single iteration 2 In the previous experiments reported on WSJ we have accidentally used bigram lattices