1 / 1

Known Non-targets for PLDA-SVM Training/Scoring

Construction of Discriminative Kernels from Known and Unknown Non-targets for PLDA-SVM Scoring. Feature Extraction and Index Randomization. Wei RAO and Man- Wai MAK Dept . of Electronic and Information Engineering, The Hong Kong Polytechnic University. Utterance Partitioning. Introduction

kitra
Download Presentation

Known Non-targets for PLDA-SVM Training/Scoring

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Construction of Discriminative Kernels from Known and Unknown Non-targets for PLDA-SVM Scoring FeatureExtraction and Index Randomization Wei RAO and Man-Wai MAK • Dept. of Electronic and Information Engineering,The Hong Kong Polytechnic University Utterance Partitioning • Introduction • Motivation • NIST 2012 SRE permits systems to use the information of other target-speakers (called known non-targets) in each verification trial. • Methods • We exploited this new protocol to enhance the performance of PLDA-SVM scoring [3], which is an effective way to utilize the multiple enrollment utterances of target speakers. We used the score vectors of both known and unknown non-targets as the impostor class data to train speaker-dependent SVMs. We also applied utterance partitioning to alleviate the imbalance between the speaker- and imposter-class data during SVM training. • Key Findings • Results show that incorporating known non-targets into the training of speaker-dependent PLDA-SVMs together with utterance partitioning can boost the performance of i-vector based PLDA systems significantly. • Methods • UnknownNon-targets for PLDA-SVM Training/Scoring • UP-AVR Background speaker’s i-vectors Background speaker’s i-vectors PLDA Scoring + Empirical Kernel Map PLDA Scoring + Empirical Kernel Map SVM Training SVM Training Target speaker’s i-vectors Target speaker’s i-vectors contains the i-vectors of the competing known non-targets with respect to s. PLDA Scoring + Empirical Kernel Map PLDA Scoring + Empirical Kernel Map Target speaker SVM Target speaker SVM Results Unknown non-targets’ i-vectors Known non-targets’ i-vectors • Methods • Empirical LR Kernel Maps Target speaker’s i-vectors Target speaker’s i-vectors Background speaker’s i-vectors Background speaker’s i-vectors Target speaker enrollment utts. Test utt. Background speaker utts. UP-AVR • KnownNon-targets for PLDA-SVM Training/Scoring I-vector Extractor PLDA Scoring + Empirical Kernel Map Results demonstrate the advantages of including known non-targets for training the SVMs (Test vector) (Speaker-class vectors) (Imposter-class vectors) References [1] P. Kenny, “Bayesian speaker verification with heavy-tailed priors”, in Proc. of Odyssey: Speaker and Language Recognition Workshop, Brno, Czech Republic, June 2010. [2] D. Garcia-Romero and C.Y. Espy-Wilson, “Analysis of i-vector length normalization in speaker recognition systems”, in Proc. Interspeech 2011, Florence, Italy, Aug. 2011, pp. 249–252. [3] M. W. Mak and W. Rao, “Likelihood-Ratio Empirical Kernels for I-Vector Based PLDA-SVM Scoring”, in Proc. ICASSP 2013, Vancouver, Canada, May 2013, pp. 7702-7706. [4] W. Rao and M.W. Mak,“Boosting the Performance of I-Vector Based Speaker Verification via Utterance Partitioning”, IEEE Trans. on Audio, Speech and Language Processing,May 2013, vol. 21, no. 5, pp. 1012-1022. PLDA score of i-vectors and EER UP-AVR is very important for SVM scoring. The performance of PLDA-SVM scoring after UP-AVR is much better than PLDA scoring.

More Related