  The performance of automated speech recognition (ASR) systems is well known
to differ for varied application domains. At the same time, vendors and
research groups typically report ASR quality results either for limited use
simplistic domains (audiobooks, TED talks), or proprietary datasets. To fill
this gap, we provide an open-source 10-hour ASR system evaluation dataset NTR
MediaSpeech for 4 languages: Spanish, French, Turkish and Arabic. The dataset
was collected from the official youtube channels of media in the respective
languages, and manually transcribed. We estimate that the WER of the dataset is
under 5%. We have benchmarked many ASR systems available both commercially and
freely, and provide the benchmark results. We also open-source baseline
QuartzNet models for each language.
