1-2hit |
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.
The glottal closure instants (GCIs) obtained from the wavelet analysis of speech signal are investigated in comparison with those obtained from the EGG signal. Experimental results have shown that about 96% of GCIs with wavelet transformed speech signal is located within 0.5 ms with respect to the GCIs with EGG signal.