  In this paper, we describe KT-Speech-Crawler: an approach for automatic
dataset construction for speech recognition by crawling YouTube videos. We
outline several filtering and post-processing steps, which extract samples that
can be used for training end-to-end neural speech recognition systems. In our
experiments, we demonstrate that a single-core version of the crawler can
obtain around 150 hours of transcribed speech within a day, containing an
estimated 3.5% word error rate in the transcriptions. Automatically collected
samples contain reading and spontaneous speech recorded in various conditions
including background noise and music, distant microphone recordings, and a
variety of accents and reverberation. When training a deep neural network on
speech recognition, we observed around 40\% word error rate reduction on the
Wall Street Journal dataset by integrating 200 hours of the collected samples
into the training set. The demo (http://emnlp-demo.lakomkin.me/) and the
crawler code (https://github.com/EgorLakomkin/KTSpeechCrawler) are publicly
available.
