Approaches to phoneme-based topic spotting: an experimental comparison

Topic spotting is often performed on the output of a large vocabulary recognizer or a keyword spotter. However, this requires detailed knowledge about the vocabulary, and transcribed training data. If portability to new topics and languages is important, then a topic spotter based on phoneme recognition is preferable. A phoneme recognizer is run on training data consisting of audio files labeled by topic alone-no word transcripts are required. Phoneme sub-sequences which help to predict the topic are then extracted automatically. The work described was carried out by two teams exploring three very different approaches to phoneme-based topic spotting: the “DP-ngram”, the “decision tree”, and the “Euclidean” approach. Results obtained by each team on the ARM (Airborne Reconnaissance Mission) and Switchboard data sets were compared by means of receiver operating characteristic (ROC) curves. The best performance for each team was obtained via a similar type of discriminative training