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Automatic Speech Recognition and Text-to-Speech systems are primarily trainedin a supervised fashion and require high-quality, accurately labeled speechdatasets. In this work, we examine common problems with speech data andintroduce a toolbox for the construction and interactive error analysis ofspeech datasets. The construction tool is based on K\"urzinger et al. work,and, to the best of our knowledge, the dataset exploration tool is the world'sfirst open-source tool of this kind. We demonstrate how to apply these tools tocreate a Russian speech dataset and analyze existing speech datasets(Multilingual LibriSpeech, Mozilla Common Voice). The tools are open sourced asa part of the NeMo framework.

A Toolbox for Construction and Analysis of Speech Datasets