  We present a freely available speech corpus for the Uzbek language and report
preliminary automatic speech recognition (ASR) results using both the deep
neural network hidden Markov model (DNN-HMM) and end-to-end (E2E)
architectures. The Uzbek speech corpus (USC) comprises 958 different speakers
with a total of 105 hours of transcribed audio recordings. To the best of our
knowledge, this is the first open-source Uzbek speech corpus dedicated to the
ASR task. To ensure high quality, the USC has been manually checked by native
speakers. We first describe the design and development procedures of the USC,
and then explain the conducted ASR experiments in detail. The experimental
results demonstrate promising results for the applicability of the USC for ASR.
Specifically, 18.1% and 17.4% word error rates were achieved on the validation
and test sets, respectively. To enable experiment reproducibility, we share the
USC dataset, pre-trained models, and training recipes in our GitHub repository.
