Latent Dirichlet Allocation Based Acoustic Data Selection for Automatic Speech Recognition
Table 5: Amount of selected data by aLDA
['Component', 'Duration (hours)', 'Percentage of domain']
For the random selection two budgets of 500 and 1000 hours are used and each experiment is repeated 2 times and an average value plus the standard deviation of the runs are provided (due to the data size and computation time this experiment could not be repeated more). Using all of the available data, the WER on the test set is 29.4. Phone-posterior based selection with a predefined budget of 1000 hours yields a WER of 29.0 which is slightly better than using all of the available data, but savings on computation time is massive (50% less data used for training the model). aLDA method selects 49.7% of the data and brings down the error by 0.9% absolute. Combing aLDA with tLDA further reduces the error to 28.3% while selecting only 108.5 hours more data.