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Vowel-like regions (VLRs) in speech includes vowels, semi-vowels, and diphthong sound units. VLR can be identified using a vowel-like region onset point (VLROP) event. By production, the VLR has impulse-like excitation and therefore information about the vocal tract system may be better manifested in them. Also, the VLR is a relatively high signal-to-noise ratio (SNR) region. Speaker information extracted from such a region may therefore be more speaker discriminative and relatively less affected by the degradations like noise, reverberation, and sensor mismatches. Due to this, better speaker modeling and reliable testing may be possible. In this paper, VLRs are detected using the knowledge of VLROPs during training and testing. Features from the VLRs are then used for training and testing the speaker models. As a result, significant improvement in the performance is reported for speaker verification under degraded conditions.