Code-Switching Speech Corpus
<p><strong>German-English Code-Switching speech dataset</strong></p> <p>We provide means to resegment a subset of the German **Spoken Wikipedia Corpus** (SWC) enabling a particular focus on code-switching. This results in the German-English code-switching corpus, a 34h transcribed speech corpus of read Wikipedia articles which can be used as a benchmark for research on code-switching. The articles are read by a large and diverse group of people. The SWC is perhaps the largest corpus of freely-available aligned speech for German. It contains 1014 spoken articles read by more than 350 identified speakers comprising 386h of speech. This corpus is available at http://nats.gitlab.io/swc.</p> <p>In SWC, since most of the articles are long, the recordings submitted by the volunteers are also long (∼54min) on average. These audio files are manually annotated at word-level and also segment level in XML format. We use a language identification tool to detect code-switching in the transcription of the audio files with consecutive indices. To extract intra-sentential code-switching segments, we ensure that the detected code-switching is preceded and followed by German words or sentences. The final set consists of 34h of speech data and 12,437 code-switching segments (in Kaldi ASR toolkit data format).</p> <p> </p> <p><strong>Citation</strong></p> <p>@article{baumann2019spoken,<br> title={The Spoken Wikipedia Corpus collection: Harvesting, alignment and an application to hyperlistening},<br> author={Baumann, Timo and K{\"o}hn, Arne and Hennig, Felix},<br> journal={Language Resources and Evaluation},<br> volume={53},<br> number={2},<br> pages={303--329},<br> year={2019},<br> publisher={Springer}<br> }</p> <p>@article{grave2018learning,<br> title={Learning word vectors for 157 languages},<br> author={Grave, Edouard and Bojanowski, Piotr and Gupta, Prakhar and Joulin, Armand and Mikolov, Tomas},<br> journal={arXiv preprint arXiv:1802.06893},<br> year={2018}<br> }</p> <p> </p>
ShareScore
28/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 8
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 0
- Engagement
- 0