SANTLR: Speech Annotation Toolkit for Low Resource Languages
This toolkit addresses the problem of data scarcity for researchers working on low-resource speech recognition, but it is incremental as it builds on existing annotation concepts with specific improvements.
The authors tackled the lack of tools for low-resource speech data collection by developing SANTLR, a web-based toolkit that enables easy collection and annotation of speech corpora through transcription or recording, resulting in a user-friendly interface and a multi-step ranking mechanism to prioritize annotations.
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.