Experimenting with Hybrid TDNN/HMM Acoustic Models for Russian Speech Recognition

In this paper, we study an application of time delay neural networks (TDNNs) in acoustic modeling for large vocabulary continuous Russian speech recognition. We created TDNNs with various numbers of hidden layers and units in the hidden layers with p-norm nonlinearity. Training of acoustic models was carried out on our own Russian speech corpus containing phonetically balanced phrases. Duration of the speech corpus is more than 30 h. Testing of TDNN-based acoustic models was performed in the very large vocabulary continuous Russian speech recognition task. Conducted experiments showed that TDNN models outperformed baseline deep neural network models in terms of the word error rate.