SNR-Invariant PLDA Modeling for Robust Speaker Verification

Slides:

Advertisements

Similar presentations

Known Non-targets for PLDA-SVM Training/Scoring Construction of Discriminative Kernels from Known and Unknown Non-targets for PLDA-SVM Scoring Results.

Advertisements

Results obtained in speaker recognition using Gaussian Mixture Models Marieta Gâta*, Gavril Toderean** *North University of Baia Mare **Technical University.

Acoustic Vector Re-sampling for GMMSVM-Based Speaker Verification

Fusion of HMM’s Likelihood and Viterbi Path for On-line Signature Verification Bao Ly Van - Sonia Garcia Salicetti - Bernadette Dorizzi Institut National.

A Text-Independent Speaker Recognition System

Automatic Identification of Bacterial Types using Statistical Image Modeling Sigal Trattner, Dr. Hayit Greenspan, Prof. Shimon Abboud Department of Biomedical.

Robust Voice Activity Detection for Interview Speech in NIST Speaker Recognition Evaluation Man-Wai MAK and Hon-Bill YU The Hong Kong Polytechnic University.

LYU0103 Speech Recognition Techniques for Digital Video Library Supervisor : Prof Michael R. Lyu Students: Gao Zheng Hong Lei Mo.

Communications & Multimedia Signal Processing Analysis of the Effects of Train noise on Recognition Rate using Formants and MFCC Esfandiar Zavarehei Department.

Speaker Adaptation for Vowel Classification

Language and Speaker Identification using Gaussian Mixture Model Prepare by Jacky Chau The Chinese University of Hong Kong 18th September, 2002.

Independent Component Analysis (ICA) and Factor Analysis (FA)

The Chinese University of Hong Kong Department of Computer Science and Engineering Lyu0202 Advanced Audio Information Retrieval System.

SNR-Dependent Mixture of PLDA for Noise Robust Speaker Verification

FYP0202 Advanced Audio Information Retrieval System By Alex Fok, Shirley Ng.

Tous droits réservés © 2005 CRIM The CRIM Systems for the NIST 2008 SRE Patrick Kenny, Najim Dehak and Pierre Ouellet Centre de recherche informatique.

Introduction Mapping Modeling Speaker Diarization Summary H. Aronowitz (IBM) Intra-Class Variability Modeling for Speech Processing June 08 1 Dr. Hagai.

Advisor: Prof. Tony Jebara

HMM-BASED PSEUDO-CLEAN SPEECH SYNTHESIS FOR SPLICE ALGORITHM Jun Du, Yu Hu, Li-Rong Dai, Ren-Hua Wang Wen-Yi Chu Department of Computer Science & Information.

Mining Discriminative Components With Low-Rank and Sparsity Constraints for Face Recognition Qiang Zhang, Baoxin Li Computer Science and Engineering Arizona.

VBS Documentation and Implementation The full standard initiative is located at Quick description Standard manual.

1 HMM - Part 2 Review of the last lecture The EM algorithm Continuous density HMM.

International Conference on Intelligent and Advanced Systems 2007 Chee-Ming Ting Sh-Hussain Salleh Tian-Swee Tan A. K. Ariff. Jain-De,Lee.

Research & development Component Score Weighting for GMM based Text-Independent Speaker Verification Liang Lu SNLP Unit, France Telecom R&D Beijing

1 Improved Speaker Adaptation Using Speaker Dependent Feature Projections Spyros Matsoukas and Richard Schwartz Sep. 5, 2003 Martigny, Switzerland.

Sound-Event Partitioning and Feature Normalization for Robust Sound-Event Detection 2 Department of Electronic and Information Engineering The Hong Kong.

Jun-Won Suh Intelligent Electronic Systems Human and Systems Engineering Department of Electrical and Computer Engineering Speaker Verification System.

Using Support Vector Machines to Enhance the Performance of Bayesian Face Recognition IEEE Transaction on Information Forensics and Security Zhifeng Li,

A Baseline System for Speaker Recognition C. Mokbel, H. Greige, R. Zantout, H. Abi Akl A. Ghaoui, J. Chalhoub, R. Bayeh University Of Balamand - ELISA.

Nick Wang, 25 Oct Speaker identification and verification using EigenVoices O. Thyes, R. Kuhn, P. Nguyen, and J.-C. Junqua in ICSLP2000 Presented.

USE OF IMPROVED FEATURE VECTORS IN SPECTRAL SUBTRACTION METHOD Emrah Besci, Semih Ergin, M.Bilginer Gülmezoğlu, Atalay Barkana Osmangazi University, Electrical.

Tom Ko and Brian Mak The Hong Kong University of Science and Technology.

HMM - Part 2 The EM algorithm Continuous density HMM.

Multi-Speaker Modeling with Shared Prior Distributions and Model Structures for Bayesian Speech Synthesis Kei Hashimoto, Yoshihiko Nankaku, and Keiichi.

A Semi-Blind Technique for MIMO Channel Matrix Estimation Aditya Jagannatham and Bhaskar D. Rao The proposed algorithm performs well compared to its training.

Speaker Identification by Combining MFCC and Phase Information Longbiao Wang (Nagaoka University of Technologyh, Japan) Seiichi Nakagawa (Toyohashi University.

Jun-Won Suh Intelligent Electronic Systems Human and Systems Engineering Department of Electrical and Computer Engineering Speaker Verification System.

Paper Reading Dalong Du Nov.27, Papers Leon Gu and Takeo Kanade. A Generative Shape Regularization Model for Robust Face Alignment. ECCV08. Yan.

Voice Activity Detection based on OptimallyWeighted Combination of Multiple Features Yusuke Kida and Tatsuya Kawahara School of Informatics, Kyoto University,

Variational Bayesian Methods for Audio Indexing

An i-Vector PLDA based Gender Identification Approach for Severely Distorted and Multilingual DARPA RATS Data Shivesh Ranjan, Gang Liu and John H. L. Hansen.

Speaker Verification Using Adapted GMM Presented by CWJ 2000/8/16.

SNR-Invariant PLDA Modeling for Robust Speaker Verification Na Li and Man-Wai Mak Department of Electronic and Information Engineering The Hong Kong Polytechnic.

Madhulika Pannuri Intelligent Electronic Systems Human and Systems Engineering Department of Electrical and Computer Engineering Correlation Dimension.

Gaussian Mixture Model classification of Multi-Color Fluorescence In Situ Hybridization (M-FISH) Images Amin Fazel 2006 Department of Computer Science.

A Study on Speaker Adaptation of Continuous Density HMM Parameters By Chin-Hui Lee, Chih-Heng Lin, and Biing-Hwang Juang Presented by: 陳亮宇 1990 ICASSP/IEEE.

Experience Report: System Log Analysis for Anomaly Detection

Support Feature Machine for DNA microarray data

Quadratic Classifiers (QC)

Deeply learned face representations are sparse, selective, and robust

Ch 12. Continuous Latent Variables ~ 12

Spectral and Temporal Modulation Features for Phonetic Recognition Stephen A. Zahorian, Hongbing Hu, Zhengqing Chen, Jiang Wu Department of Electrical.

Feature Mapping FOR SPEAKER Diarization IN NOisy conditions

Outlier Processing via L1-Principal Subspaces

Orthogonal Subspace Projection - Matched Filter

Statistical Models for Automatic Speech Recognition

3. Applications to Speaker Verification

Learning with information of features

ECE539 final project Instructor: Yu Hen Hu Fall 2005

Statistical Models for Automatic Speech Recognition

SMEM Algorithm for Mixture Models

PCA is “an orthogonal linear transformation that transfers the data to a new coordinate system such that the greatest variance by any projection of the.

Decision Making Based on Cohort Scores for

Generally Discriminant Analysis

A maximum likelihood estimation and training on the fly approach

Biointelligence Laboratory, Seoul National University

EM Algorithm and its Applications

EM Algorithm 主講人：虞台文.

Probabilistic Surrogate Models

Stochastic Methods.

Presentation transcript:

SNR-Invariant PLDA Modeling for Robust Speaker Verification Na Li and Man-Wai Mak Interspeech 2015 Dresden, Germany Department of Electronic and Information Engineering The Hong Kong Polytechnic University, Hong Kong SAR, China

Contents SNR-invariant PLDA modeling for Robust Speaker Verification Background and Motivation of Work SNR-invariant PLDA modeling for Robust Speaker Verification Experiments on SRE12 Conclusions 2

Background I-vector/PLDA Framework m : Global mean of all i-vectors V : Bases of speaker subspace : Latent speaker factor with a standard normal distribution : Residual term follows a Gaussian distribution with zero mean and full covariance : Length-normalized i-vector of speaker i

Background In conventional multi-condition training, we pool i-vectors from various background noise levels to train m, V and Σ. I-vectors with 2 SNR ranges EM Algorithm

Motivation We argue that the variation caused by SNR can be modeled by an SNR subspace and utterances falling within a narrow SNR range should share the same set of SNR factors. SNR Subspace SNR Factor 1 Group1 SNR Factor 2 Group2 SNR Factor 3 Group3

Motivation Method of modeling SNR information i-vector 6 dB SNR Subspace 15 dB clean I-vector Space

Distribution of SNR in SRE12 Each SNR region is handled by a specific set of SNR factors

Contents Background Motivation of Work SNR-invariant PLDA modeling for Robust Speaker Verification Experiments on SRE12 Conclusions

SNR-invariant PLDA PLDA: By adding an SNR factor to the conventional PLDA, we have SNR-invariant PLDA: where U denotes the SNR subspace, is an SNR factor, and is the speaker (identity) factor for speaker i. Note that it is not the same as PLDA with channel subspace: i: Speaker index j: Session index k: SNR index

SNR-invariant PLDA We separate I-vectors into different groups according to the SNR of their utterances EM Algorithm

Compared with Conventional PLDA SNR-Invariant PLDA

PLDA vs SNR-invariant PLDA Generative Model PLDA SNR-invariant PLDA

PLDA vs SNR-invariant PLDA E-Step PLDA SNR-invariant PLDA

PLDA versus SNR-invariant PLDA M-Step PLDA SNR-invariant PLDA

SNR-invariant PLDA Score Likelihood Ratio Scores

Contents Motivation of Work Conventional PLDA Mixture of PLDA for Noise Robust Speaker Verification Experiments on SRE12 Conclusions

Data and Features Evaluation dataset: Common evaluation condition 1 and 4 of NIST SRE 2012 core set. Parameterization: 19 MFCCs together with energy plus their 1st and 2nd derivatives  60-Dim UBM: gender-dependent, 1024 mixtures Total Variability Matrix: gender-dependent, 500 total factors I-Vector Preprocessing: Whitening by WCCN then length normalization Followed by NFA (500-dim  200-dim)

Finding SNR Groups Training Utterances

SNR Distributions SNR Distribution of training and test utterances in CC4 Training Utterances Test Utterances

Performance on SRE12 CC1 Mixture of PLDA (Mak, Interspeech14) Method Parameters Male Female K Q EER(%) minDCF PLDA - 5.42 0.371 7.53 0.531 mPLDA 5.28 0.415 7.70 0.539 SNR-Invariant PLDA 3 40 0.382 6.93 0.528 5 0.381 6.89 0.522 6 5.29 0.388 6.90 0.536 8 30 5.56 0.384 7.05 0.545 No. of SNR factors (dim of ) No. of SNR Groups

Performance on SRE12 CC4 Method Parameters Male Female K Q EER(%) minDCF PLDA - 3.13 0.312 2.82 0.341 mPLDA 2.88 0.329 2.71 0.332 SNR-Invariant PLDA 3 40 2.72 0.289 2.36 0.314 5 2.67 0.291 2.38 0.322 6 2.63 0.287 2.43 0.319 8 30 2.70 0.292 2.29 0.313 No. of SNR factors (dim of ) No. of SNR Groups

Performance on SRE12 Conventional PLDA CC4, Female SNR-Invariant PLDA

Conclusions We show that while I-vectors of different SNR fall on different regions of the I-vector space, they vary within a single cluster in an SNR-subspace. Therefore, it is possible to model the SNR variability by adding an SNR loading matrix and SNR factors to the conventional PLDA model.