1. A method, comprising: converting a plurality of feature vectors that represents a speech utterance into a plurality of log probability sets, the converting using a classifier ensemble including a plurality of classifiers; transforming the plurality of log probability sets into a plurality of output symbol sequences; combining the plurality of output symbol sequences, using an iterative a priori probability calculation algorithm, into a fusion output symbol sequence; and retrieving one or more speech utterances from a speech database using the plurality of output symbol sequences.
1. A method, comprising: converting a plurality of feature vectors that represents a speech utterance into a plurality of log probability sets, the converting using a classifier ensemble including a plurality of classifiers; transforming the plurality of log probability sets into a plurality of output symbol sequences; combining the plurality of output symbol sequences, using an iterative a priori probability calculation algorithm, into a fusion output symbol sequence; and retrieving one or more speech utterances from a speech database using the plurality of output symbol sequences. 7. The method of claim 1 , wherein the transforming is performed by decoder ensemble, which includes a Viterbi decoder having a wildcard probability category, the wildcard probability category to capture a symbol of one of the output symbol sequences that has no clear winning symbol probability.