# SANTLR: Speech Annotation Toolkit for Low Resource Languages

arXiv: https://arxiv.org/abs/1908.01067 · HTML: https://mlsanity.com/paper/1908.01067/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2019.

## Built on by (most cited first, 5 of 5)

- [NoiseQA: Challenge Set Evaluation for User-Centric Question Answering](https://mlsanity.com/paper/2102.08345/) (2021)
- [GPTs Are Multilingual Annotators for Sequence Generation Tasks](https://mlsanity.com/paper/2402.05512/) (2024)
- [On the Impact of Speech Recognition Errors in Passage Retrieval for Spoken Question Answering](https://mlsanity.com/paper/2209.12944/) (2022)
- [Cost Analysis of Human-corrected Transcription for Predominately Oral Languages](https://mlsanity.com/paper/2510.12781/) (2025)
- [Dealing with the Hard Facts of Low-Resource African NLP](https://mlsanity.com/paper/2511.18557/) (2025)

## Abstract (arXiv)

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.