A Survey of Recent DNN Architectures on the TIMIT Phone Recognition Task This paper was supported by the project no. P103/12/G084 of the Grant Agency of the Czech Republic and by the grant of the University of West Bohemia, project No. SGS-2016-039. Access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum provided under the programme "Projects of Large Research, Development, and Innovations Infrastructures" (CESNET LM2015042), is greatly appreciated.
Table 2: TDNN Phone Error Rate
['Context Size', '# Filters', 'Development PER Min', 'Development PER Max', 'Development PER Avg', 'Test PER Min', 'Test PER Max', 'Test PER Avg']
All the results are worse than the results for our FF networks. Interestingly enough, we obtained the best results for context sizes 5,5,5,5. The larger context sizes resulted in worse network performance. Also, the networks with higher number of filters have better performance. The best average PER we have received was 18.20 % for the network with the context size 5,5,5,5 and 512 filters in each layer.