Abstract
In the paper, we describe a research of DNN-based acoustic modeling for Russian speech recognition. Training and testing of the system was performed using the open-source Kaldi toolkit. We created tanh and p-norm DNNs with a different number of hidden layers and a different number of hidden units of tanh DNNs. Testing of the models was carried out on very large vocabulary continuous Russian speech recognition task. We obtained a relative WER reduction of 20 % comparing to the baseline GMM-HMM system.
Author supplied keywords
Cite
CITATION STYLE
Kipyatkova, I., & Karpov, A. (2016). Dnn-based acoustic modeling for Russian speech recognition using Kaldi. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9811 LNCS, pp. 246–253). Springer Verlag. https://doi.org/10.1007/978-3-319-43958-7_29
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
