Contaminated speech training methods for robust DNN-HMM distant speech recognition
Table 1: Phone Error Rate (PER%) of the baseline system.
['[ITALIC] TestTrain', 'APASCI (6 h)', 'Euronews (100 h)']
This section reports the results obtained with a standard DNN-HMM system, after optimizing the main ASR free-parameters on the dev-set. The best performance is reached with a DNN composed of 6 hidden layers with 1500 neurons per layer, adopting a symmetric context window of 17 frames (8 before and 8 after the current frame) and using a learning rate of 0.008. Note that such free-parameters are used for both the training corpora and for all the addressed acoustic scenarios, due to a similar outcome of the optimization step. Only a difference concerning the optimal number of tied-states is observed, leading to 2.5k and 5k tied-states for APASCI and Euronews, respectively. This loss of performance further confirms how challenging DSR is under adverse acoustic conditions and without any help from grammars or LMs. It is also highly relevant that with only 6 hours of high-quality speech material a performance similar to that of a 100 hours corpus is obtained.