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In this paper, we have investigated recurrent deep neural networks (DNNs) incombination with regularization techniques as dropout, zoneout, andregularization post-layer. As a benchmark, we chose the TIMIT phone recognitiontask due to its popularity and broad availability in the community. It alsosimulates a low-resource scenario that is helpful in minor languages. Also, weprefer the phone recognition task because it is much more sensitive to anacoustic model quality than a large vocabulary continuous speech recognitiontask. In recent years, recurrent DNNs pushed the error rates in automaticspeech recognition down. But, there was no clear winner in proposedarchitectures. The dropout was used as the regularization technique in mostcases, but combination with other regularization techniques together with modelensembles was omitted. However, just an ensemble of recurrent DNNs performedbest and achieved an average phone error rate from 10 experiments 14.84 %(minimum 14.69 %) on core test set that is slightly lower then thebest-published PER to date, according to our knowledge. Finally, in contrast ofthe most papers, we published the open-source scripts to easily replicate theresults and to help continue the development.

Recurrent DNNs and its Ensembles on the TIMIT Phone Recognition Task