Speaker Verification by Vowel and Nonvowel Like Segmentation

This work proposes methods for detecting vowel-like regions (VLRs) and non-vowel-like regions (non-VLRs) using excitation source information. The VLR onset and end points are hypothesized and used in an iterative algorithm for detecting the VLRs. Next, for detection of non-VLRs, the linear prediction (LP) residual samples in the VLRs are attenuated significantly to indirectly emphasize the residual samples in the non-VLRs. The modified LP residual samples excite the time varying all pole filter to reconstruct non-VLRs enhanced speech and used for detecting non-VLRs. The VLRs and non-VLRs are used independently during training and testing of a speaker verification (SV) system to reduce gross level mismatch due to sound units and achieve better compensation of degradation effects by applying different normalization to these two different energy regions. Finally, the scores are combined with higher weight on VLRs, which are more speaker specific. Experiments verify that the proposed approach provides improved performance for clean and degraded speech. On the NIST-2003 speaker recognition database, using VLRs and non-VLRs improves the equal error rate from 6.63% to 6% and from 2.29% to 1.89% for a GMM-UBM based and an i-vector based SV system, respectively.