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 3: LSTM Phone Error Rate
['Network', 'Development PER Min', 'Development PER Max', 'Development PER Avg', 'Test PER Min', 'Test PER Max', 'Test PER Avg']
Our LSTM networks have better performance than other network types used in this work. We have received the best average PER, 15.58 %, for the network with 4 hidden layers each with 1024 LSTM units. The best PER from all experiments was 15.02 %, also belonging to the network with 4 hidden layers with 1024 LSTM units. However, we have also trained other networks with similar PER to the best one. The network with 3 layers with 1024 units and the network with 5 layers with 1024 units have the second and third best PER, 15.69 % and 15.71 % respectively. The networks with 256 units in each layer have comparable or worse PER than FF networks. The network with 6 layers with 1024 units has worse performance than less deep layers due to overfitting.