A new feature parameter based on a discrete wavelet transform is proposed for word boundary detection of isolated utterances. The sum of standard deviation of wavelet coefficients in the third coarse and weighted first detailed scale is defined as a new feature parameter for endpoint detection. Experimental results demonstrate the superiority of the proposed feature to the conventional ones in capturing word boundaries even in noisy speech.