Segmental Score Fusion for Text-independent Speaker Verification

1. Segmental Score Fusion for Text-independent Speaker Verification http://diuf.unifr.ch/diva Asmaa El Hannani 1, Dijana Petrovska-Delacrétaz1, Raphael Blouet2 and Gerard Chollet2 1: Diva Group, University of Fribourg, Switzerland; 2: Ecole Nationale Supérieure des Télécommunication, France. 1. Introduction Previous studies have shown that phonemes have different discriminant power for the speaker verification task. In order to better exploit these differences, it seems reasonable to segment the speech in distinct speech classes and carry out the speaker modeling for each class separately. The proposed method combines the DTW distortion measure with data-driven segmentation based on ALISP tools, and uses a Logistic Regression Function to determine the optimal fusion weights of the speech segments. 2. Unsupervised Automatic Language Independent Speech Processing (ALISP) unit acquisition, and their HMM modeling 3. Segmental DTW system based on searching in a client and world speech dictionaries. During the test phase, each test speech datais first segmentedwith the 64 ALISP HMM models, then, each of the test speechsegments is compared, with a DTW distance measure, to theClient-Dictionary and to the World-Dictionary. Instead of the widely used phonetic labels,data-driven labelsautomatically determined from the training corpusare used. 4. Applying Logistic Regression for Segmental ScoreFusion 5. Speaker verification results for the linear fusion and the fusion using the Logistic Regression approach. The performance of the proposed system is evaluatedon subsets build from NIST' 2002 Evaluation data. The score, S, of the claimed speaker iscalculated as: M is the number of segments found in the test speech data. tis the weight given to the segment ytofthe class t 6. Conclusion Our resultsshow that applying score fusion with the weights found by the LogisticRegression function leads to better results compared to a simplesummation of the segmental scores. Our method could also be applied toautomatically remove the less significant segments (usuallycorresponding to ``silence'' segments). Asmaa.elhannani@unifr.ch