SANTLR: Speech Annotation Toolkit for Low Resource Languages

Xinjian Li, Zhong Zhou, Siddharth Dalmia, Alan W. Black, Florian Metze


While low resource speech recognition has attracted a lot of attention from the speech community, there are a few tools available to facilitate low resource speech collection. In this work, we present SANTLR: Speech Annotation Toolkit for Low Resource Languages. It is a web-based toolkit which allows researchers to easily collect and annotate a corpus of speech in a low resource language. Annotators may use this toolkit for two purposes: transcription or recording. In transcription, annotators would transcribe audio files provided by the researchers; in recording, annotators would record their voice by reading provided texts. We highlight two properties of this toolkit. First, SANTLR has a very user-friendly User Interface (UI). Both researchers and annotators may use this simple web interface to interact. There is no requirement for the annotators to have any expertise in audio or text processing. The toolkit would handle all preprocessing and postprocessing steps. Second, we employ a multi-step ranking mechanism facilitate the annotation process. In particular, the toolkit would give higher priority to utterances which are easier to annotate and are more beneficial to achieving the goal of the annotation, e.g. quickly training an acoustic model.


Cite as: Li, X., Zhou, Z., Dalmia, S., Black, A.W., Metze, F. (2019) SANTLR: Speech Annotation Toolkit for Low Resource Languages. Proc. Interspeech 2019, 3681-3682.


@inproceedings{Li2019,
  author={Xinjian Li and Zhong Zhou and Siddharth Dalmia and Alan W. Black and Florian Metze},
  title={{SANTLR: Speech Annotation Toolkit for Low Resource Languages}},
  year=2019,
  booktitle={Proc. Interspeech 2019},
  pages={3681--3682}
}