Topic Identification From Audio Recordings Using Rich Recognition Results And Neural Network Based Classifiers

This paper investigates the use of a Neural Network classifier for topic identification from conversational telephone speech, which exploits rich recognition results coming from an automatic speech recognizer. The baseline features used to feed the neural classifier are produced using the words extracted from the 1-best sequence. Rich recognition results include the word union of the first n-best sequences, the consensus hypothesis and the full or pruned Word Confusion Network generated from the n-best sequences. Different probabilistic information attached to the words, including confidence and word posterior probabilities, is investigated together with classical and probabilistic feature weighting schemes. A large experimentation on conversational telephone speech of Fisher corpus is reported, showing significant improvements when compared to the state of the art.