100000-word recognition using acoustic-segment networks

Speech recognition for a vocabulary of 100000 words is described. Acoustic-segment networks are used as word templates in recognition. The acoustic-segment networks are automatically generated from orthographic strings of the words using rules that account for several kinds of variations in speech. To reduce the amount of computation in recognition, a tree representation of the networks and a preselection method based on input-frame sampling are used. It is confirmed that 98.75% of the computation can be eliminated without a significant increase of error, when using the preselection which outputs 500 candidates for main matching. Top-20 recognition accuracy is 93.5% for 10000 test utterances of five males and five females.<<ETX>>