Table of Contents

• Understanding individual-level speech variability: From novel speech production data to robust speaker recognitionShrikanth S. Narayanan. [doi]
• I-Vector Representation Based on GMM and DNN for Audio ClassificationNajim Dehak. [doi]
• Voice conversion and spoofing countermeasures for speaker verificationHaizhou Li 0001. [doi]
• A Low-Power Text-Dependent Speaker Verification System with Narrow-Band Feature Pre-Selection and Weighted Dynamic Time WarpingQing He, Gregory W. Wornell, Wei Ma. 1-8 [doi]
• Deep Neural Network based Text-Dependent Speaker Verification : Preliminary ResultsGautam Bhattacharya, Patrick Kenny, Md. Jahangir Alam, Themos Stafylakis. 9-15 [doi]
• Uncertainty Modeling Without Subspace Methods For Text-Dependent Speaker RecognitionPatrick Kenny, Themos Stafylakis, Md. Jahangir Alam, Vishwa Gupta, Marcel Kockmann. 16-23 [doi]
• Deep Neural Networks and Hidden Markov Models in i-vector-based Text-Dependent Speaker VerificationHossein Zeinali, Lukás Burget, Hossein Sameti, Ondrej Glembek, Oldrich Plchot. 24-30 [doi]
• Fast Scoring for PLDA with Uncertainty PropagationWei-Wei Lin, Man-Wai Mak. 31-38 [doi]
• I-vector transformation and scaling for PLDA based speaker recognitionSandro Cumani, Pietro Laface. 39-46 [doi]
• Rapid Computation of I-vectorLongting Xu, Kong-Aik Lee, Haizhou Li 0001, Zhen Yang. 47-52 [doi]
• Constrained discriminative speaker verification specific to normalized i-vectorsPierre-Michel Bousquet, Jean-François Bonastre. 53-59 [doi]
• Iterative Bayesian and MMSE-based noise compensation techniques for speaker recognition in the i-vector spaceWaad Ben Kheder, Driss Matrouf, Moez Ajili, Jean-François Bonastre. 60-67 [doi]
• Between-Class Covariance Correction For Linear Discriminant Analysis in Language RecognitionAbhinav Misra, Qian Zhang, Finnian Kelly, John H. L. Hansen. 68-73 [doi]
• Incorporating uncertainty as a Quality Measure in I-Vector Based Language RecognitionAmir Hossein Poorjam, Rahim Saeidi, Tomi Kinnunen, Ville Hautamäki. 74-80 [doi]
• Discriminating Languages in a Probabilistic Latent SubspaceAleksandr Sizov, Kong-Aik Lee, Tomi Kinnunen. 81-88 [doi]
• Investigation of Senone-based Long-Short Term Memory RNNs for Spoken Language RecognitionYao Tian, Liang He 0003, Yi Liu, Jia Liu 0001. 89-93 [doi]
• Automatic Accent Recognition Systems and the Effects of Data on PerformanceGeorgina Brown. 94-100 [doi]
• The "Sprekend Nederland" project and its application to accent locationDavid A. van Leeuwen, Rosemary Orr. 101-108 [doi]
• Deep Language: a comprehensive deep learning approach to end-to-end language recognitionTrung Ngo Trong, Ville Hautamäki, Kong-Aik Lee. 109-116 [doi]
• On the use of phone-gram units in recurrent neural networks for language identificationChristian Salamea, Luis Fernando D'Haro, Ricardo de Córdoba, Rubén San Segundo. 117-123 [doi]
• Language Recognition for Dialects and Closely Related LanguagesGregory Gelly, Jean-Luc Gauvain, Lori Lamel, Antoine Laurent, Viet Bac Le, Abdel Messaoudi. 124-131 [doi]
• Identification of British English regional accents using fusion of i-vector and multi-accent phonotactic systemsMaryam Najafian, Saeid Safavi 0001, Phil Weber, Martin J. Russell. 132-139 [doi]
• Improvements on Deep Bottleneck Network based I-Vector Representation for Spoken Language IdentificationYan Song, Ruilian Cui, Ian Vince McLoughlin, Li-Rong Dai. 140-145 [doi]
• Deep complementary features for speaker identification in TV broadcast dataMateusz Budnik, Ali Khodabakhsh, Laurent Besacier, Cenk Demiroglu. 146-151 [doi]
• First investigations on self trained speaker diarizationGaël Le Lan, Sylvain Meignier, Delphine Charlet, Anthony Larcher. 152-157 [doi]
• Soft VAD in Factor Analysis Based Speaker Segmentation of Broadcast NewsBrecht Desplanques, Kris Demuynck, Jean-Pierre Martens. 158-165 [doi]
• BAT System Description for NIST LRE 2015Oldrich Plchot, Pavel Matejka, Ondrej Glembek, Radek Fér, Ondrej Novotný, Jan Pesán, Lukás Burget, Niko Brummer, Sandro Cumani. 166-173 [doi]
• The IBM 2016 Speaker Recognition SystemSeyed Omid Sadjadi, Sriram Ganapathy, Jason W. Pelecanos. 174-180 [doi]
• The Sheffield language recognition system in NIST LRE 2015Raymond W. M. Ng, Mauro Nicolao, Oscar Saz, Madina Hasan, Bhusan Chettri, Mortaza Doulaty, Tan Lee, Thomas Hain. 181-187 [doi]
• Analyzing the Effect of Channel Mismatch on the SRI Language Recognition Evaluation 2015 SystemMitchell McLaren, Diego Castán, Luciana Ferrer. 188-195 [doi]
• The MITLL NIST LRE 2015 Language Recognition SystemPedro A. Torres-Carrasquillo, Najim Dehak, Elizabeth Godoy, Douglas A. Reynolds, Fred Richardson, Stephen Shum, Elliot Singer, Douglas E. Sturim. 196-203 [doi]
• Augmented Data Training of Joint Acoustic/Phonotactic DNN i-vectors for NIST LRE15Alan McCree, Gregory Sell, Daniel Garcia-Romero. 204-209 [doi]
• LID-senone Extraction via Deep Neural Networks for End-to-End Language IdentificationMa Jin, Yan Song, Ian Vince McLoughlin, Li-Rong Dai, Zhongfu Ye. 210-216 [doi]
• On autoencoders in the i-vector space for speaker recognitionTimur Pekhovsky, Sergey Novoselov, Aleksei Sholohov, Oleg Kudashev. 217-224 [doi]
• Channel Compensation for Speaker Recognition using MAP Adapted PLDA and Denoising DNNsFred Richardson, Brian E. Nemsick, Douglas A. Reynolds. 225-230 [doi]
• Evaluation of an LSTM-RNN System in Different NIST Language Recognition FrameworksRubén Zazo, Alicia Lozano-Diez, Joaquin Gonzalez-Rodriguez. 231-236 [doi]
• Feature-based likelihood ratios for speaker recognition from linguistically-constrained formant-based i-vectorsJavier Franco-Pedroso, Joaquin Gonzalez-Rodriguez. 237-244 [doi]
• Improving Robustness of Speaker Verification Against Mimicked SpeechKuruvachan K. George, Santhosh Kumar C, K. I. Ramachandran, Ashish Panda. 245-251 [doi]
• Multi-channel i-vector combination for robust speaker verification in multi-room domestic environmentsAlessio Brutti, Alberto Abad. 252-258 [doi]
• Voice Liveness Detection for Speaker Verification based on a Tandem Single/Double-channel Pop Noise DetectorSayaka Shiota, Fernando Villavicencio, Junichi Yamagishi, Nobutaka Ono, Isao Echizen, Tomoko Matsui. 259-263 [doi]
• A PLDA Approach for Language and Text Independent Speaker RecognitionAbbas Khosravani, Mohammad Mehdi Homayounpour, Dijana Petrovska-Delacrétaz, Gérard Chollet. 264-269 [doi]
• Spoofing Detection on the ASVspoof2015 Challenge Corpus Employing Deep Neural NetworksMd. Jahangir Alam, Patrick Kenny, Vishwa Gupta, Themos Stafylakis. 270-276 [doi]
• Age-Related Voice Disguise and its Impact on Speaker Verification AccuracyRosa González Hautamäki, Md. Sahidullah, Tomi Kinnunen, Ville Hautamäki. 277-282 [doi]
• A New Feature for Automatic Speaker Verification Anti-Spoofing: Constant Q Cepstral CoefficientsMassimiliano Todisco, Héctor Delgado, Nicholas W. D. Evans. 283-290 [doi]
• Multi-Bit Allocation: Preparing Voice Biometrics for Template ProtectionMarco Paulini, Christian Rathgeb, Andreas Nautsch, Hermine Reichau, Herbert Reininger, Christoph Busch. 291-296 [doi]
• Summary of the 2015 NIST Language Recognition i-Vector Machine Learning ChallengeAudrey Tong, Craig S. Greenberg, Alvin F. Martin, Désiré Bansé, John M. Howard, Hui Zhao, George R. Doddington, Daniel Garcia-Romero, Alan McCree, Douglas A. Reynolds, Elliot Singer, Jaime Hernandez-Cordero, Lisa P. Mason. 297-302 [doi]
• Out-of-Set i-Vector Selection for Open-set Language IdentificationHamid Behravan, Tomi Kinnunen, Ville Hautamäki. 303-310 [doi]
• I2R Submission to the 2015 NIST Language Recognition I-vector ChallengeHanwu Sun, Trung Hieu Nguyen, Guangsen Wang, Kong-Aik Lee, Bin Ma, Haizhou Li 0001. 311-318 [doi]
• A Semisupervised Approach for Language Identification based on Ladder NetworksEhud Ben-Reuven, Jacob Goldberger. 319-325 [doi]
• Cantonese forensic voice comparison with higher-level features: likelihood ratio-based validation using F-pattern and tonal F0 trajectories over a disyllabic hexaphonePhil Rose, Bruce Xiao Wang. 326-333 [doi]
• I-Vectors for speech activity detectionElie Khoury, Matt Garland. 334-339 [doi]
• Compensation for phonetic nuisance variability in speaker recognition using DNNsThemos Stafylakis, Patrick Kenny, Vishwa Gupta, Md. Jahangir Alam, Marcel Kockmann. 340-345 [doi]
• Local binary patterns as features for speaker recognitionWaad Ben Kheder, Driss Matrouf, Moez Ajili, Jean-François Bonastre. 346-351 [doi]
• Analysis and Optimization of Bottleneck Features for Speaker RecognitionAlicia Lozano-Diez, Anna Silnova, Pavel Matejka, Ondrej Glembek, Oldrich Plchot, Jan Pesán, Lukás Burget, Joaquin Gonzalez-Rodriguez. 352-357 [doi]
• Robustness of Quality-based Score Calibration of Speaker Recognition Systems with respect to low-SNR and short-duration conditionsAndreas Nautsch, Rahim Saeidi, Christian Rathgeb, Christoph Busch. 358-365 [doi]
• From Features to Speaker Vectors by means of Restricted Boltzmann Machine AdaptationPooyan Safari, Omid Ghahabi, Javier Hernando. 366-371 [doi]
• Reducing Noise Bias in the i-Vector Space for Speaker RecognitionYosef A. Solewicz, Hagai Aronowitz, Timo Becker. 372-376 [doi]
• Semi-supervised On-line Speaker Diarization for Meeting Data with Incremental Maximum A-posteriori AdaptationGiovanni Soldi, Massimiliano Todisco, Héctor Delgado, Christophe Beaugeant, Nicholas W. D. Evans. 377-384 [doi]
• Influence of transition cost in the segmentation stage of speaker diarizationBeatriz Martínez-González, José M. Pardo, Rubén San Segundo, J. M. Montero. 385-392 [doi]
• Analysis of the Impact of the Audio Database Characteristics in the Accuracy of a Speaker Clustering SystemJesús Jorrín-Prieto, Carlos Vaquero, Paola García. 393-399 [doi]
• Short- and Long-Term Speech Features for Hybrid HMM-i-Vector based Speaker Diarization SystemAbraham Woubie Zewoudie, Jordi Luque, Javier Hernando. 400-406 [doi]
• On the Use of PLDA i-vector Scoring for Clustering Short SegmentsItay Salmun, Irit Opher, Itshak Lapidot. 407-414 [doi]