Unsupervised Word Discovery: Boundary Detection with Clustering vs. Dynamic Programming

• URL: http://arxiv.org/abs/2409.14486v1
• Date: Sun, 22 Sep 2024 15:16:43 GMT
• Title: Unsupervised Word Discovery: Boundary Detection with Clustering vs. Dynamic Programming
• Authors: Simon Malan, Benjamin van Niekerk, Herman Kamper,
• Abstract summary: We look at the long-standing problem of segmenting unlabeled speech into word-like segments and clustering these into a lexicon. 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.
• Score: 22.044042563954378
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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