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Speaker-Phonetic I-Vector Modeling for Text-Dependent Speaker Verification with Random Digit Strings Shengyu YAO Ruohua ZHOU Pengyuan ZHANG Publication IEICE TRANSACTIONS on Information and Systems Vol.E102-D No.2 pp.346-354 Publication Date: 2019/02/01 Publicized: 2018/11/19 Online ISSN: 1745-1361 DOI: 10.1587/transinf.2018EDP7310 Type of Manuscript: PAPER Category: Speech and Hearing Keyword: speaker verification, text-dependent, speaker-phonetic, random digit strings, i-vector, phonetically-constrained, Full Text: PDF(1.2MB)>>
Summary: This paper proposes a speaker-phonetic i-vector modeling method for text-dependent speaker verification with random digit strings, in which enrollment and test utterances are not of the same phrase. The core of the proposed method is making use of digit alignment information in i-vector framework. By utilizing force alignment information, verification scores of the testing trials can be computed in the fixed-phrase situation, in which the compared speech segments between the enrollment and test utterances are of the same phonetic content. Specifically, utterances are segmented into digits, then a unique phonetically-constrained i-vector extractor is applied to obtain speaker and channel variability representation for every digit segment. Probabilistic linear discriminant analysis (PLDA) and s-norm are subsequently used for channel compensation and score normalization respectively. The final score is obtained by combing the digit scores, which are computed by scoring individual digit segments of the test utterance against the corresponding ones of the enrollment. Experimental results on the Part 3 of Robust Speaker Recognition (RSR2015) database demonstrate that the proposed approach significantly outperforms GMM-UBM by 52.3% and 53.5% relative in equal error rate (EER) for male and female respectively. | ||||||||||
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