9th Annual Conference of the International Speech Communication Association

Brisbane, Australia
September 22-26, 2008

Investigations into Phonological Attribute Classifier Representations for CRF Phone Recognition

Prateeti Mohapatra, Eric Fosler-Lussier

Ohio State University, USA

Classifier combination has long been a staple for improving robustness of ASR systems; we present an experiment where introducing phonological feature scores from another lab's system [1] into our system gives a statistically significant improvement in Conditional Random Field-based TIMIT phone recognition, despite a standalone system based on their features performing significantly worse. The second part of the paper explores the reasons for this improvement by examining different representations of phonological attribute classifiers, in terms of what they are classifying (binary versus n-ary features) and representation of scoring functions. The analysis leads to the conclusions that while binary phonological feature estimates usually are worse than n-ary features, the combination of the two can be quite good if there are also differences in the feature definitions or training paradigm.

Full Paper

Bibliographic reference.  Mohapatra, Prateeti / Fosler-Lussier, Eric (2008): "Investigations into phonological attribute classifier representations for CRF phone recognition", In INTERSPEECH-2008, 2558-2561.