Latent Dirichlet Allocation Based Acoustic Data Selection for Automatic Speech Recognition
Table 3: Baseline results
['Model', 'WER Overall', 'WER Far-field', 'WER Close-talk']
The purpose of this study is to show how aLDA data selection can improve ASR accuracy of a target domain and for that reason all of the model architectures are the same in all of the experiments and the only difference is the amount of data used for training the acoustic models. During decoding a pruned 3-gram language model was used to generate lattices and the lattices were then rescored using a 5-gram language model. WER for the far-field subset of the test set is very high and that shows how challenging this dataset is.