A directory listing retrieval system based on connected letter recognition

Automatic speech recognition has advanced to the stage where it is now possible to recognize connected strings of words (e.g. digits, letters, city names, airline terms) from a word reference set of isolated tokens of each of the words in the vocabulary. Recently an improved training technique, called embedded word training, was proposed in which reference word patterns were extracted from within connected word sequences themselves. In this investigation we extend the embedded word training procedure to handle letters of the alphabet for use in a directory listing retrieval task. By performing connected letter recognition of spoken names based on letter classes (rather than specific letters themselves) we show how reliable name recognition results can be achieved using a fairly straightforward name search procedure. We have tested the recognition system on 200 randomly chosen names (chosen from an 18,210 name directory) spoken at a normal rate by 4 talkers (3 male, 1 female) in a speaker trained mode. We have found that an 8% improvement in name recognition accuracy is obtained when using embedded letter training patterns over that obtained from isolated letter patterns alone. The overall name recognition accuracy was close to 95%.