A speaker independent word recognition based on transient matching

In speaker independent word recognition, time-normalization problem has not been completely solved at present. In the attempt to solve this problem, this paper proposes an algorithm which takes into consideration nearly invariant properties in the time axis of the transient parts of the speech. Using this algorithm, speaker independent recognition becomes possible by performing multi-dimensional analysis of the transient parts of the speech after transient detection. This algorithm has been applied to 12-word recognition and 101-monosyllable recognition, showing correct rates of about 98 % and 82 %, respectively, for non-learning data.