Papers › A Toolbox for Construction and Analysis of Speech Datasets

A Toolbox for Construction and Analysis of Speech Datasets

11 Apr 2021arXiv:2104.04896archive 2025-07-28

Evelina Bakhturina, Vitaly Lavrukhin, Boris Ginsburg

Automatic Speech Recognition and Text-to-Speech systems are primarily trained in a supervised fashion and require high-quality, accurately labeled speech datasets. In this work, we examine common problems with speech data and introduce a toolbox for the construction and interactive error analysis of speech 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's first open-source tool of this kind. We demonstrate how to apply these tools to create a Russian speech dataset and analyze existing speech datasets (Multilingual LibriSpeech, Mozilla Common Voice). The tools are open sourced as a part of the NeMo framework.

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lumaku/ctc-segmentation mentioned on GitHubpytorchApache-2.0 report

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Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Speech RecognitionText to Speechspeech-recognitiontext-to-speech

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