fauxneticien/qbe-std_feats_eval

Evaluation of feature extraction methods for query-by-example spoken term detection with low resource languages # Evaluation of feature extraction methods for query-by-example spoken term detection with low resource languages In this project we examine different feature extraction methods (Kaldi MFCCs, BUT/Phonexia Bottleneck features, and variants of wav2vec 2.0) for performing QbE-STD with data from language documentation projects. A walkthrough of the entire experiment pipeline can be found in scripts/README.md. Links to acrhived experiment artefacts uploaded to Zenodo are provided in the last section of this README file. A description of the analyses based on the data is found in analyses/README.md, which also provides links to the pilot analyses with a multilingual model, system evaluations, and the error analysis (all viewable online as GitHub Markdown). ## Citation ```bibtex @misc{san2021leveraging, title={Leveraging pre-trained representations to improve access to untranscribed speech from endangered languages}, author={San, Nay and Bartelds, Martijn and Browne, Mitchell and Clifford, Lily and Gibson, Fiona and Mansfield, John and Nash, David and Simpson, Jane and Turpin, Myfany and Vollmer, Maria and Wilmoth, Sasha and Jurafsky, Dan}, year={2021}, eprint={2103.14583}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Directory structure The directory structure for this project roughly follows the Cookiecutter Data Science guidelines. ``` ├── README.md <- This top-level README ├── docker-compose.yml <- Configurations for launching Docker containers ├── qbe-std_feats_eval.Rproj <- RStudio project file, used to get repository path using R's 'here' package ├── requirements.txt <- Python package requirements ├── tmp/ <- Empty directory to download zip files into, if required ├── data/ │ ├── raw/ <- Immutable data, not modified by scripts │ │ ├── datasets/ <- Audio data and ground truth labels placed here │ │ ├── model_checkpoints/ <- wav2vec 2.0 model …