Ninth International Conference on Spoken Language Processing

Pittsburgh, PA, USA
September 17-21, 2006

Single Frame Selection for Phoneme Classification

Tingyao Wu, Dirk Van Compernolle, Jacques Duchateau, Hugo Van Hamme

Katholieke Universiteit Leuven, Belgium

Our former study [1] has shown that maximum likelihood (ML) based frame selection, which selects reliable frames from a high resolution along the time axis, helps to improve the discrimination between phonemes. In this paper, we present our recent research on single frame selection for a phoneme classification task. A new single selection, which only selects one frame for one state in an Hidden Markov Model (HMM), is proposed. The new technique takes likelihoods of frames and their positions in a phoneme segment into account at the same time, and selects very few frames to represent the spectral evolution of the phoneme. Furthermore, we also show that for a low model complexity, a phoneme model trained by selected frames is more discriminative than a model using all frames.

Full Paper

Bibliographic reference.  Wu, Tingyao / Compernolle, Dirk Van / Duchateau, Jacques / Hamme, Hugo Van (2006): "Single frame selection for phoneme classification", In INTERSPEECH-2006, paper 1247-Mon3CaP.2.