  While the Kaldi framework provides state-of-the-art components for speech
recognition like feature extraction, deep neural network (DNN)-based acoustic
models, and a weighted finite state transducer (WFST)-based decoder, it is
difficult to implement a new flexible DNN model. By contrast, a general-purpose
deep learning framework, such as TensorFlow, can easily build various types of
neural network architectures using a tensor-based computation method, but it is
difficult to apply them to WFST-based speech recognition. In this study, a
TensorFlow-based acoustic model is integrated with a WFST-based Kaldi decoder
to combine the two frameworks. The features and alignments used in Kaldi are
converted so they can be trained by the TensorFlow model, and the DNN-based
acoustic model is then trained. In the integrated Kaldi decoder, the posterior
probabilities are calculated by querying the trained TensorFlow model, and a
beam search is performed to generate the lattice. The advantages of the
proposed one-pass decoder include the application of various types of neural
networks to WFST-based speech recognition and WFST-based online decoding using
a TensorFlow-based acoustic model. The TensorFlow based acoustic models trained
using the RM, WSJ, and LibriSpeech datasets show the same level of performance
as the model trained using the Kaldi framework.
