6 papers
ZeroSyl: Simple Zero-Resource Syllable Tokenization for Spoken Language Modeling
Nicol Visser, Simon Malan, Danel Slabbert +1
Pure speech language models aim to learn language directly from raw audio without textual resources. A key challenge is that discrete tokens from self-supervised speech encoders re…
Recovering the Zipfian Distribution in Unsupervised Term Discovery
Danel Slabbert, Simon Malan, Herman Kamper
Unsupervised term discovery involves segmenting unlabelled speech into word- or syllable-like units and clustering these into a lexicon of candidate types. True lexicons follow a Z…
Revisiting Lexicon Evaluation in Unsupervised Word Discovery
Simon Malan, Danel Slabbert, Herman Kamper
Building a lexicon from discovered word-like units is a central goal in zero-resource speech processing. But do our evaluations provide a trustworthy indication of lexicon quality?…
Unsupervised lexicon learning from speech is limited by representations rather than clustering
Danel Slabbert, Simon Malan, Herman Kamper
Zero-resource word segmentation and clustering systems aim to tokenise speech into word-like units without access to text labels. Despite progress, the induced lexicons are still f…
Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery?
Simon Malan, Benjamin van Niekerk, Herman Kamper
We investigate the problem of segmenting unlabeled speech into word-like units and clustering these to create a lexicon. Prior work can be categorized into two frameworks. Bottom-u…
Unsupervised Word Discovery: Boundary Detection with Clustering vs. Dynamic Programming
Simon Malan, Benjamin van Niekerk, Herman Kamper
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 couple…