kamperh/recipe_bucktsong_awe

Unsupervised acoustic word embeddings evaluated on Buckeye English and NCHLT Xitsonga data in Python 2.7. Unsupervised Acoustic Word Embeddings on Buckeye English and NCHLT Xitsonga =========================================================================== Overview -------- **Note:** An updated version of this recipe is available at kamperh/recipe_bucktsong_aw…. This updated recipe is implemented in Python 3 (instead of Python 2.7) and uses LibROSA for feature extraction (instead of HTK). Unsupervised acoustic word embedding (AWE) approaches are implemented and evaluated on the Buckeye English and NCHLT Xitsonga speech datasets. The experiments are described in: - H. Kamper, "Truly unsupervised acoustic word embeddings using weak top-down constraints in encoder-decoder models," in *Proc. ICASSP*, 2019. [arXiv] Please cite this paper if you use the code. Disclaimer ---------- The code provided here is not pretty. But I believe that research should be reproducible. I provide no guarantees with the code, but please let me know if you have any problems, find bugs or have general comments. Datasets -------- Portions of the Buckeye English and NCHLT Xitsonga corpora are used. The whole Buckeye corpus will be required to execute the steps here, and the portion of the NCHLT data. These can be downloaded from: - Buckeye corpus: buckeyecorpus.osu.edu - NCHLT Xitsonga portion: www.zerospeech.com. This requires registration for the challenge. From the complete Buckeye corpus we split off several subsets. The most important are the sets labelled as `devpart1` and `zs` in the code here. These sets respectively correspond to `English1` and `English2` in Kamper et al., 2016, so see the paper for more details. More details of which speakers are found in which set is also given at the end of features/readme.md. We use the entire Xitsonga dataset provided as part of the Zero Speech Challenge 2015 (this is already a subset of the NCHLT data). Download all these datasets beforehand. These can be stored apart from the code. Clone the repository ---------- …