PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

An Online Algorithm for Hierarchical Phoneme Classification
Ofer Dekel, Joseph Keshet and Yoram Singer
In: Workshop on Multimodal Interaction and Related Machine Learning Algorithms (MLMI 04), 21-23 June 2004, Martigny, Switzerland Martigny, Switzerland.

Abstract

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.

EPrint Type:Conference or Workshop Item (Lecture)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Speech
Theory & Algorithms
ID Code:879
Deposited By:Joseph Keshet
Deposited On:03 January 2005