Unsupervised Word Discovery: Boundary Detection with Clustering vs Dynamic Programming
<|reference_start|>Unsupervised Word Discovery: Boundary Detection with Clustering vs Dynamic Programming: We look at the long-standing problem of segmenting unlabeled speech into word-like segments and clustering these into a lexicon. Several previous methods use a scoring model coupled with dynamic programming to find an optimal segmentation. Here we propose a much simpler strategy: we predict word boundaries using the dissimilarity between adjacent self-supervised features, then we cluster the predicted segments to construct a lexicon. For a fair comparison, we update the older ES-KMeans dynamic programming method with better features and boundary constraints. On the five-language ZeroSpeech benchmarks, our simple approach gives similar state-of-the-art results compared to the new ES-KMeans+ method, while being almost five times faster.<|reference_end|>
@article{malan2024unsupervised,
    title={Unsupervised Word Discovery: Boundary Detection with Clustering vs.
  Dynamic Programming},
    author={Simon Malan, Benjamin van Niekerk, Herman Kamper},
    journal={arXiv preprint arXiv:2409.14486},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14486},
    primaryClass={eess.AS cs.CL cs.SD}
}