  State-of-the-art hybrid automatic speech recognition (ASR) system exploits
deep neural network (DNN) based acoustic models (AM) trained with Lattice
Free-Maximum Mutual Information (LF-MMI) criterion and n-gram language models.
The AMs typically have millions of parameters and require significant parameter
reduction to operate on embedded devices. The impact of parameter quantization
on the overall word recognition performance is studied in this paper. Following
approaches are presented: (i) AM trained in Kaldi framework with conventional
factorized TDNN (TDNN-F) architecture, (ii) the TDNN AM built in Kaldi loaded
into the PyTorch toolkit using a C++ wrapper for post-training quantization,
(iii) quantization-aware training in PyTorch for Kaldi TDNN model, (iv)
quantization-aware training in Kaldi. Results obtained on standard Librispeech
setup provide an interesting overview of recognition accuracy w.r.t. applied
quantization scheme.
