An online algorithm for hierarchical phoneme classification

Ofer Dekel, Joseph Keshet, Yoram Singer

Research output: Contribution to journalConference articlepeer-review

37 Scopus citations


We present an algorithmic framework for phoneme classification where the set of phonemes is organized in a predefined hierarchical structure. This structure is encoded via a rooted tree which induces a metric over the set of phonemes. Our approach combines techniques from large margin kernel methods and Bayesian analysis. Extending the notion of large margin to hierarchical classification, we associate a prototype with each individual phoneme and with each phonetic group which corresponds to a node in the tree. We then formulate the learning task as an optimization problem with margin constraints over the phoneme set. In the spirit of Bayesian methods, we impose similarity requirements between the prototypes corresponding to adjacent phonemes in the phonetic hierarchy. We describe a new online algorithm for solving the hierarchical classification problem and provide worst-case loss analysis for the algorithm. We demonstrate the merits of our approach by applying the algorithm to synthetic data and as well as speech data.

Original languageEnglish (US)
Pages (from-to)146-158
Number of pages13
StatePublished - 2005
EventFirst International Workshop on Machine Learning for Multimodal Interaction, MLMI 2004 - Martigny, Switzerland
Duration: Jun 21 2004Jun 23 2004

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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