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This paper is a description of the Speech Technology Center (STC) automatic speech recognition (ASR) system for the "VOiCES from a Distance Challenge 2019". We participated in the Fixed condition of the ASR task, which means that the only training data available was an 80-hour subset of the Lib-riSpeech corpus. The main difficulty of the challenge is a mismatch between clean training data and distant noisy development/evaluation data. In order to tackle this, we applied room acoustics simulation and weighted prediction error (WPE) dereverberation. We also utilized well-known speaker adaptation using x-vector speaker embeddings, as well as novel room acoustics adaptation with R-vector room impulse response (RIR) embeddings. The system used a lattice-level combination of 6 acoustic models based on different pronunciation dictionaries and input features. N-best hypotheses were rescored with 3 neural network language models (NNLMs) trained on both words and sub-word units. NNLMs were also explored for out-of-vocabulary (OOV) words handling by means of artificial texts generation. The final system achieved Word Error Rate (WER) of 14.7% on the evaluation data, which is the best result in the challenge.

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