Exemplar based recognition systems are characterized by the fact

that, instead of abstracting large amounts of data into compact models,

they store the observed data enriched with some annotations and

infer on-the-fly from the data by finding those exemplars that resemble

the input speech best. One advantage of exemplar based systems

is that next to deriving what the current phone or word is, one can

easily derive a wealth of meta-information concerning the chunk of

audio under investigation. In this work we harvest meta-information

from the set of best matching exemplars, that is thought to be relevant

for the recognition such as word boundary predictions and

speaker entropy. Integrating this meta-information into the recognition

framework using segmental conditional random fields, reduced

the WER of the exemplar based system on the WSJ Nov92 20k task

from 8.2% to 7.6%. Adding the HMM-score and multiple HMM

phone detectors as features further reduced the error rate to 6.6%.

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In  ICASSP

Publisher  IEEE

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