Speaker Normalization Based on Piecewise Linear Frequency Warping

An efficient algorithm for speaker-independent spoken word recognition is presented. This algorithm is based on the time-frequency warping with inter-frame consistency, where each frame of an input pattern is mapped to a reference pattern by controlling the mapping of several points (pivots) on the frame. The mapping of non-pivot points is given by linear interpolation between mapping of two consecutive pivots. The optimal mapping is obtained by using a dynamic programming based algorithm. The computational complexity of the algorithm is reduced to less than that of the previous time-frequency warping algorithm with inter-frame consistency. Experimental results show advantageous characteristics of the present algorithm