Nearest neighbour decision rule for vowel and digit recognition

Minimum distance to mean is usually used as a classification rule in speech and speaker recognition studies. In this paper it is shown that the nearest neighbour decision rule gives significant improvement in classification score for vowel and digit recognition schemes. Autocorrelation coefficients of lags two to five sampling instants are used to form the feature vector. Pour samples per class have been used. Minimum squared Euclidean distance of the test vector from the nearest reference is chosen as the classification rule. For sustained vowels the recognition score is cent percent. for the same feature the minimum distance to mean gives 70 % recognition score. When the reference samples of a given speaker is tested over the vowels spoken by different speaker(up to 10), this scheme gives the recognition score of about 95 %. for digits without any time warping the recognition score of about 86 % to 92 % is obtained.