5 papers
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…
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis
Marc-André Carbonneau, Benjamin van Niekerk, Hugo Seuté +3
Modeling voice identity is challenging due to its multifaceted nature. In generative speech systems, identity is often assessed using automatic speaker verification (ASV) embedding…
LinearVC: Linear transformations of self-supervised features through the lens of voice conversion
Herman Kamper, Benjamin van Niekerk, Julian Zaïdi +1
We introduce LinearVC, a simple voice conversion method that sheds light on the structure of self-supervised representations. First, we show that simple linear transformations of s…
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…