Reducing Domain Mismatch by Maximum Mean Discrepancy Based Autoencoders

Size: px
Start display at page:

Download "Reducing Domain Mismatch by Maximum Mean Discrepancy Based Autoencoders"

Transcription

1 Reducing Domain Mismatch by Maximum Mean Discrepancy Based Autoencoders Wei-wei Lin, Man-Wai Mak, Longxin Li The Hong Kong Polytechnic University Jen-Tzung Chien National Chiao Tung University

2 Contributions We show how maximum mean discrepancy (MMD) can be generalized to measure the discrepancies among multiple distributions. We propose a new domain adaptation method based on MMD and demonstrated that it can greatly reduce multisource variability. 2

3 Process of Speaker Verification Utterance from registered speaker low-dim representation of the whole utterance Spectral Analysis 60-dim acoustic vectors Factor Analysis 500-dim i-vector Decision Threshold x s Spectral Analysis 60-dim acoustic vectors Factor Analysis PLDA Scoring x t 500-dim i-vector Decision Making Utterance from test speaker 3

4 I-Vectors Speaker supervector UBM Supervector Total variability matrix Total variability factor I-vector is the maximum-a-posteriori (MAP) estimate of, which we denoted as. Instead of using high-dimension supervector to represent a speaker, we use more compact (low-dimension) i-vector to represent a speaker. represents the subspace where i-vectors vary. 4

5 I-Vector/PLDA Procedure of i-vector/plda: MFCC i-vector extractor Preprocessing PLDA Modeling In Gaussian PLDA, a preprocessed i-vector from the j-th session of speaker i is considered generated from a factor analysis model: Pre-processed i-vector Mean of i-vectors in training set Speaker subspace Speaker factor Residue 5

6 Given a test i-vector and target-speaker s i-vectors, the verification score is the log-likelihood ratio between two hypotheses: log S LR (x s, x t ) = log I-Vector/PLDA p(x s, x t Same speaker) p(x s, x t di erent speaker) = 1 2 xt s Qx s + x T s Px t xt t Qx t + const where 6

7 Domain Mismatch NIST SRE16 is a multilingual dataset for speaker verification. Test data include Cantonese and Tagalog speakers. But both Cantonese and Tagalog speech in the development set are unlabeled and small in number (2344 segments). 7

8 Domain Mismatch Means Covariance matrices Pairwise normalized distance between different languages and genders 8

9 Domain Mismatch We have English corpora from previous SREs and SWB, which are large in number and have speaker labels. But the language mismatch in SRE16 makes these corpora less useful. We aim to adapt the i-vectors of English speech to look more like the i-vectors of Cantonese and Tagalog. Then, we use the adapted English i-vectors to train a PLDA model for scoring Cantonese and Tagalog i-vectors. 9

10 Domain Adaptation I-vector based domain adaptation: Enrolment MFCC Test MFCC I-vector extraction Project into common feature space MMD-based Autoencoder Preprocessing PLDA scoring 10

11 IDVC Inter-dataset variability compensation (IDVC) is a popular domain adaptation technique for speaker verification. IDVC aims to remove the subspace that causes most of the interdataset variability: where is an i-vector, the columns of comprise the eigenvectors of the covariance matrix of the domain means. 11

12 Motivations of Our Work A drawback of IDVC is that the domain mismatch is entirely defined by the domain means. From the perspective of reducing the divergence between probabilistic distributions, this is not enough. 12

13 Motivations of Our Work Means are the first moment of probabilistic distributions only. Even if two distributions have exactly the same mean, they could still be very different, due to the difference in the higher order statistics. 13

14 Maximum Mean Discrepancy The theoretical work in domain adaptation suggests that it is important to have a good measurement of the divergence between the data distributions of different domains. Maximum mean discrepancy (MMD) is a distance measure in the space of probability. Given two datasets, MMD computes the mean squared difference between the statistics of the two datasets: 14

15 Maximum Mean Discrepancy Kernel function 15

16 Maximum Mean Discrepancy Assume that we have D sets of data, where. We can generalize MMD to measure the discrepancies among multiple domains: Kernel function 16

17 <latexit sha1_base64="3lawrkpcbyj7dol/fbup23wbqde=">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</latexit> <latexit sha1_base64="3lawrkpcbyj7dol/fbup23wbqde=">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</latexit> <latexit sha1_base64="3lawrkpcbyj7dol/fbup23wbqde=">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</latexit> <latexit sha1_base64="3lawrkpcbyj7dol/fbup23wbqde=">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</latexit> Domain-Invariant Autoencoders The domain-invariant autoencoder (DAE) directly encodes the features that minimize the multi-source mismatch: Domain 1 Domain 2 Domain 3 D =3 17

18 <latexit sha1_base64="izhrlqw7iixuukgi6e7jbrou+a8=">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</latexit> <latexit sha1_base64="izhrlqw7iixuukgi6e7jbrou+a8=">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</latexit> <latexit sha1_base64="izhrlqw7iixuukgi6e7jbrou+a8=">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</latexit> <latexit sha1_base64="izhrlqw7iixuukgi6e7jbrou+a8=">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</latexit> <latexit sha1_base64="izhrlqw7iixuukgi6e7jbrou+a8=">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</latexit> <latexit sha1_base64="izhrlqw7iixuukgi6e7jbrou+a8=">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</latexit> <latexit sha1_base64="izhrlqw7iixuukgi6e7jbrou+a8=">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</latexit> <latexit sha1_base64="izhrlqw7iixuukgi6e7jbrou+a8=">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</latexit> Nuisance-Attribute Autoencoders The nuisance-attribute autoencoder (NAE) borrows the idea of IDVC in that it removes the domain specific features using: where g(f(x)) should contain all of the domain-specific info. Therefore, will become domain-indistinguishable. g(f(x)) is realized by an autoencoder called NAE, which encodes the features that cause most of the multi-source mismatch. 18

19 <latexit sha1_base64="3lawrkpcbyj7dol/fbup23wbqde=">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</latexit> <latexit sha1_base64="3lawrkpcbyj7dol/fbup23wbqde=">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</latexit> <latexit sha1_base64="3lawrkpcbyj7dol/fbup23wbqde=">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</latexit> <latexit sha1_base64="3lawrkpcbyj7dol/fbup23wbqde=">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</latexit> Nuisance-Attribute Autoencoders Domain 1 Domain 2 Domain 3 D =3 19

20 MMD-Baesd Autoencoders Domain 1 Domain 1 Domain 2 Domain 2 Domain 3 Domain 3 Domain-Invariant Autoencoder (DAE) Nuisance-Attribute Autoencoder (NAE)

21 t-sne Visualizations of Learned Features Before DAE Transformation After DAE Transformation The t-sne plot of the hidden activations of DAE has less domain-clustering effect than that of the i-vectors, which shows that the DAE indeed learns a domain-invariant representation. 21

22 Experimental Setup Parameterization: 19 MFCCs together with energy plus their 1 st and 2 nd derivatives à 60-Dim UBM: gender-dependent, 512 mixtures, trained by SRE16-dev Total Variability Matrix: gender-independent, 300 total factors, trained by SRE16-dev DAE- and NAE-transformed vectors: 300-dim I-Vector Preprocessing: PCA to 200-dim followed by length normalization PLDA: 200 latent factors 22

23 Experimental Setup We have conducted two sets of experiments 1. domain adaptation experiment 2. domain robustness experiment. In the domain adaptation experiment, i-vectors derived from SRE04--SRE10 and SRE16-dev were used for training the DAE, the NAE and the projection matrices in IDVC. I-vectors derived from SRE16-eval were used for testing. 23

24 Domain Adaptation Experiment Method EER mcprim acprim No Adapt IDVC DAE NAE Pooling genders and languages All of the domain adaptation methods improve system performance significantly. Both DAE and NAE outperform IDVC by a small margin. 24

25 Domain Robustness Experiment In the domain robustness experiment, for each gender and language (TGL/CAN) in test sessions, we exclude the speech of the same gender who speak that language from training. Test Data Male Training Data Female ENG TGL CAN ENG TGL CAN Male TGL Male CAN Female TGL Female CAN 25

26 Domain Robustness Experiment IDVC DAE The performance of DA methods degrades when in-domain data are excluded from training. 26

27 Domain Robustness Experiment EER (%) DAE achieves a relative reduction of 5-6% with respect to IDVC on Cantonese speech. But no gain is found on Tagalog speech. 27

28 I-vector Adaptation + PLDA Interpolation I-vectors adaptation can be combined with unsupervised PLDA model interpolation (interpolate the covariance matrices, Garcia- Romero (2014)). Method EER mcprim acprim No Adapt IDVC DAE NAE Method EER mcprim acprim No Adapt IDVC DAE NAE Without PLDA Interpolation With PLDA Interpolation,!=0.3 Combining i-vector adaptation and PLDA covariance matrix adaptation and can further improve performance. 28

29 Conclusions We proposed two MMD-based autoencoders. We show the relative improvement of 11.8% EER in the NIST 2016 SRE compared to PLDA without adaptation. We also found that MMD-based autoencoders are more robust to unseen domains. In the domain robustness experiments, MMD-based autoencoders show 5.2% and 6.8% improvement over IDVC for male and female Cantonese speakers, respectively. 29

DOMAIN MISMATCH COMPENSATION FOR SPEAKER RECOGNITION USING A LIBRARY OF WHITENERS. Elliot Singer and Douglas Reynolds

DOMAIN MISMATCH COMPENSATION FOR SPEAKER RECOGNITION USING A LIBRARY OF WHITENERS. Elliot Singer and Douglas Reynolds DOMAIN MISMATCH COMPENSATION FOR SPEAKER RECOGNITION USING A LIBRARY OF WHITENERS Elliot Singer and Douglas Reynolds Massachusetts Institute of Technology Lincoln Laboratory {es,dar}@ll.mit.edu ABSTRACT

More information

ADVANCES IN DEEP NEURAL NETWORK APPROACHES TO SPEAKER RECOGNITION

ADVANCES IN DEEP NEURAL NETWORK APPROACHES TO SPEAKER RECOGNITION ADVANCES IN DEEP NEURAL NETWORK APPROACHES TO SPEAKER RECOGNITION Mitchell McLaren 1, Yun Lei 1, Luciana Ferrer 2 1 Speech Technology and Research Laboratory, SRI International, California, USA 2 Departamento

More information

A study of speaker adaptation for DNN-based speech synthesis

A study of speaker adaptation for DNN-based speech synthesis A study of speaker adaptation for DNN-based speech synthesis Zhizheng Wu, Pawel Swietojanski, Christophe Veaux, Steve Renals, Simon King The Centre for Speech Technology Research (CSTR) University of Edinburgh,

More information

A NOVEL SCHEME FOR SPEAKER RECOGNITION USING A PHONETICALLY-AWARE DEEP NEURAL NETWORK. Yun Lei Nicolas Scheffer Luciana Ferrer Mitchell McLaren

A NOVEL SCHEME FOR SPEAKER RECOGNITION USING A PHONETICALLY-AWARE DEEP NEURAL NETWORK. Yun Lei Nicolas Scheffer Luciana Ferrer Mitchell McLaren A NOVEL SCHEME FOR SPEAKER RECOGNITION USING A PHONETICALLY-AWARE DEEP NEURAL NETWORK Yun Lei Nicolas Scheffer Luciana Ferrer Mitchell McLaren Speech Technology and Research Laboratory, SRI International,

More information

UTD-CRSS Systems for 2012 NIST Speaker Recognition Evaluation

UTD-CRSS Systems for 2012 NIST Speaker Recognition Evaluation UTD-CRSS Systems for 2012 NIST Speaker Recognition Evaluation Taufiq Hasan Gang Liu Seyed Omid Sadjadi Navid Shokouhi The CRSS SRE Team John H.L. Hansen Keith W. Godin Abhinav Misra Ali Ziaei Hynek Bořil

More information

arxiv: v2 [cs.cv] 30 Mar 2017

arxiv: v2 [cs.cv] 30 Mar 2017 Domain Adaptation for Visual Applications: A Comprehensive Survey Gabriela Csurka arxiv:1702.05374v2 [cs.cv] 30 Mar 2017 Abstract The aim of this paper 1 is to give an overview of domain adaptation and

More information

Phonetic- and Speaker-Discriminant Features for Speaker Recognition. Research Project

Phonetic- and Speaker-Discriminant Features for Speaker Recognition. Research Project Phonetic- and Speaker-Discriminant Features for Speaker Recognition by Lara Stoll Research Project Submitted to the Department of Electrical Engineering and Computer Sciences, University of California

More information

Learning Methods in Multilingual Speech Recognition

Learning Methods in Multilingual Speech Recognition Learning Methods in Multilingual Speech Recognition Hui Lin Department of Electrical Engineering University of Washington Seattle, WA 98125 linhui@u.washington.edu Li Deng, Jasha Droppo, Dong Yu, and Alex

More information

Modeling function word errors in DNN-HMM based LVCSR systems

Modeling function word errors in DNN-HMM based LVCSR systems Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford

More information

Speaker recognition using universal background model on YOHO database

Speaker recognition using universal background model on YOHO database Aalborg University Master Thesis project Speaker recognition using universal background model on YOHO database Author: Alexandre Majetniak Supervisor: Zheng-Hua Tan May 31, 2011 The Faculties of Engineering,

More information

A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation

A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation SLSP-2016 October 11-12 Natalia Tomashenko 1,2,3 natalia.tomashenko@univ-lemans.fr Yuri Khokhlov 3 khokhlov@speechpro.com Yannick

More information

Modeling function word errors in DNN-HMM based LVCSR systems

Modeling function word errors in DNN-HMM based LVCSR systems Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford

More information

Python Machine Learning

Python Machine Learning Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled

More information

Speech Recognition at ICSI: Broadcast News and beyond

Speech Recognition at ICSI: Broadcast News and beyond Speech Recognition at ICSI: Broadcast News and beyond Dan Ellis International Computer Science Institute, Berkeley CA Outline 1 2 3 The DARPA Broadcast News task Aspects of ICSI

More information

Speech Emotion Recognition Using Support Vector Machine

Speech Emotion Recognition Using Support Vector Machine Speech Emotion Recognition Using Support Vector Machine Yixiong Pan, Peipei Shen and Liping Shen Department of Computer Technology Shanghai JiaoTong University, Shanghai, China panyixiong@sjtu.edu.cn,

More information

Spoofing and countermeasures for automatic speaker verification

Spoofing and countermeasures for automatic speaker verification INTERSPEECH 2013 Spoofing and countermeasures for automatic speaker verification Nicholas Evans 1, Tomi Kinnunen 2 and Junichi Yamagishi 3,4 1 EURECOM, Sophia Antipolis, France 2 University of Eastern

More information

IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 17, NO. 3, MARCH

IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 17, NO. 3, MARCH IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 17, NO. 3, MARCH 2009 423 Adaptive Multimodal Fusion by Uncertainty Compensation With Application to Audiovisual Speech Recognition George

More information

Probabilistic Latent Semantic Analysis

Probabilistic Latent Semantic Analysis Probabilistic Latent Semantic Analysis Thomas Hofmann Presentation by Ioannis Pavlopoulos & Andreas Damianou for the course of Data Mining & Exploration 1 Outline Latent Semantic Analysis o Need o Overview

More information

Analysis of Emotion Recognition System through Speech Signal Using KNN & GMM Classifier

Analysis of Emotion Recognition System through Speech Signal Using KNN & GMM Classifier IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-issn: 2278-2834,p- ISSN: 2278-8735.Volume 10, Issue 2, Ver.1 (Mar - Apr.2015), PP 55-61 www.iosrjournals.org Analysis of Emotion

More information

WHEN THERE IS A mismatch between the acoustic

WHEN THERE IS A mismatch between the acoustic 808 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 14, NO. 3, MAY 2006 Optimization of Temporal Filters for Constructing Robust Features in Speech Recognition Jeih-Weih Hung, Member,

More information

Digital Signal Processing: Speaker Recognition Final Report (Complete Version)

Digital Signal Processing: Speaker Recognition Final Report (Complete Version) Digital Signal Processing: Speaker Recognition Final Report (Complete Version) Xinyu Zhou, Yuxin Wu, and Tiezheng Li Tsinghua University Contents 1 Introduction 1 2 Algorithms 2 2.1 VAD..................................................

More information

Likelihood-Maximizing Beamforming for Robust Hands-Free Speech Recognition

Likelihood-Maximizing Beamforming for Robust Hands-Free Speech Recognition MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Likelihood-Maximizing Beamforming for Robust Hands-Free Speech Recognition Seltzer, M.L.; Raj, B.; Stern, R.M. TR2004-088 December 2004 Abstract

More information

Calibration of Confidence Measures in Speech Recognition

Calibration of Confidence Measures in Speech Recognition Submitted to IEEE Trans on Audio, Speech, and Language, July 2010 1 Calibration of Confidence Measures in Speech Recognition Dong Yu, Senior Member, IEEE, Jinyu Li, Member, IEEE, Li Deng, Fellow, IEEE

More information

Lecture 1: Machine Learning Basics

Lecture 1: Machine Learning Basics 1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3

More information

Speech Segmentation Using Probabilistic Phonetic Feature Hierarchy and Support Vector Machines

Speech Segmentation Using Probabilistic Phonetic Feature Hierarchy and Support Vector Machines Speech Segmentation Using Probabilistic Phonetic Feature Hierarchy and Support Vector Machines Amit Juneja and Carol Espy-Wilson Department of Electrical and Computer Engineering University of Maryland,

More information

Assignment 1: Predicting Amazon Review Ratings

Assignment 1: Predicting Amazon Review Ratings Assignment 1: Predicting Amazon Review Ratings 1 Dataset Analysis Richard Park r2park@acsmail.ucsd.edu February 23, 2015 The dataset selected for this assignment comes from the set of Amazon reviews for

More information

Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models

Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models Stephan Gouws and GJ van Rooyen MIH Medialab, Stellenbosch University SOUTH AFRICA {stephan,gvrooyen}@ml.sun.ac.za

More information

BUILDING CONTEXT-DEPENDENT DNN ACOUSTIC MODELS USING KULLBACK-LEIBLER DIVERGENCE-BASED STATE TYING

BUILDING CONTEXT-DEPENDENT DNN ACOUSTIC MODELS USING KULLBACK-LEIBLER DIVERGENCE-BASED STATE TYING BUILDING CONTEXT-DEPENDENT DNN ACOUSTIC MODELS USING KULLBACK-LEIBLER DIVERGENCE-BASED STATE TYING Gábor Gosztolya 1, Tamás Grósz 1, László Tóth 1, David Imseng 2 1 MTA-SZTE Research Group on Artificial

More information

Non intrusive multi-biometrics on a mobile device: a comparison of fusion techniques

Non intrusive multi-biometrics on a mobile device: a comparison of fusion techniques Non intrusive multi-biometrics on a mobile device: a comparison of fusion techniques Lorene Allano 1*1, Andrew C. Morris 2, Harin Sellahewa 3, Sonia Garcia-Salicetti 1, Jacques Koreman 2, Sabah Jassim

More information

Speaker Recognition For Speech Under Face Cover

Speaker Recognition For Speech Under Face Cover INTERSPEECH 2015 Speaker Recognition For Speech Under Face Cover Rahim Saeidi, Tuija Niemi, Hanna Karppelin, Jouni Pohjalainen, Tomi Kinnunen, Paavo Alku Department of Signal Processing and Acoustics,

More information

Support Vector Machines for Speaker and Language Recognition

Support Vector Machines for Speaker and Language Recognition Support Vector Machines for Speaker and Language Recognition W. M. Campbell, J. P. Campbell, D. A. Reynolds, E. Singer, P. A. Torres-Carrasquillo MIT Lincoln Laboratory, 244 Wood Street, Lexington, MA

More information

Generative models and adversarial training

Generative models and adversarial training Day 4 Lecture 1 Generative models and adversarial training Kevin McGuinness kevin.mcguinness@dcu.ie Research Fellow Insight Centre for Data Analytics Dublin City University What is a generative model?

More information

International Journal of Computational Intelligence and Informatics, Vol. 1 : No. 4, January - March 2012

International Journal of Computational Intelligence and Informatics, Vol. 1 : No. 4, January - March 2012 Text-independent Mono and Cross-lingual Speaker Identification with the Constraint of Limited Data Nagaraja B G and H S Jayanna Department of Information Science and Engineering Siddaganga Institute of

More information

OCR for Arabic using SIFT Descriptors With Online Failure Prediction

OCR for Arabic using SIFT Descriptors With Online Failure Prediction OCR for Arabic using SIFT Descriptors With Online Failure Prediction Andrey Stolyarenko, Nachum Dershowitz The Blavatnik School of Computer Science Tel Aviv University Tel Aviv, Israel Email: stloyare@tau.ac.il,

More information

Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model

Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model Xinying Song, Xiaodong He, Jianfeng Gao, Li Deng Microsoft Research, One Microsoft Way, Redmond, WA 98052, U.S.A.

More information

A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation

A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation Chunpeng Wu 1, Wei Wen 1, Tariq Afzal 2, Yongmei Zhang 2, Yiran Chen 3, and Hai (Helen) Li 3 1 Electrical and

More information

Semi-Supervised GMM and DNN Acoustic Model Training with Multi-system Combination and Confidence Re-calibration

Semi-Supervised GMM and DNN Acoustic Model Training with Multi-system Combination and Confidence Re-calibration INTERSPEECH 2013 Semi-Supervised GMM and DNN Acoustic Model Training with Multi-system Combination and Confidence Re-calibration Yan Huang, Dong Yu, Yifan Gong, and Chaojun Liu Microsoft Corporation, One

More information

Investigation on Mandarin Broadcast News Speech Recognition

Investigation on Mandarin Broadcast News Speech Recognition Investigation on Mandarin Broadcast News Speech Recognition Mei-Yuh Hwang 1, Xin Lei 1, Wen Wang 2, Takahiro Shinozaki 1 1 Univ. of Washington, Dept. of Electrical Engineering, Seattle, WA 98195 USA 2

More information

Edinburgh Research Explorer

Edinburgh Research Explorer Edinburgh Research Explorer Personalising speech-to-speech translation Citation for published version: Dines, J, Liang, H, Saheer, L, Gibson, M, Byrne, W, Oura, K, Tokuda, K, Yamagishi, J, King, S, Wester,

More information

Automatic Speaker Recognition: Modelling, Feature Extraction and Effects of Clinical Environment

Automatic Speaker Recognition: Modelling, Feature Extraction and Effects of Clinical Environment Automatic Speaker Recognition: Modelling, Feature Extraction and Effects of Clinical Environment A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy Sheeraz Memon

More information

Attributed Social Network Embedding

Attributed Social Network Embedding JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, MAY 2017 1 Attributed Social Network Embedding arxiv:1705.04969v1 [cs.si] 14 May 2017 Lizi Liao, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua Abstract Embedding

More information

Transfer Learning with Applications

Transfer Learning with Applications Transfer Learning with Applications Sinno Jialin Pan 1, Qiang Yang 2,3 and Wei Fan 3 1 Institute for Infocomm Research, Singapore 2 Hong Kong University of Science and Technology 3 Huawei Noah's Ark Research

More information

LOW-RANK AND SPARSE SOFT TARGETS TO LEARN BETTER DNN ACOUSTIC MODELS

LOW-RANK AND SPARSE SOFT TARGETS TO LEARN BETTER DNN ACOUSTIC MODELS LOW-RANK AND SPARSE SOFT TARGETS TO LEARN BETTER DNN ACOUSTIC MODELS Pranay Dighe Afsaneh Asaei Hervé Bourlard Idiap Research Institute, Martigny, Switzerland École Polytechnique Fédérale de Lausanne (EPFL),

More information

DNN ACOUSTIC MODELING WITH MODULAR MULTI-LINGUAL FEATURE EXTRACTION NETWORKS

DNN ACOUSTIC MODELING WITH MODULAR MULTI-LINGUAL FEATURE EXTRACTION NETWORKS DNN ACOUSTIC MODELING WITH MODULAR MULTI-LINGUAL FEATURE EXTRACTION NETWORKS Jonas Gehring 1 Quoc Bao Nguyen 1 Florian Metze 2 Alex Waibel 1,2 1 Interactive Systems Lab, Karlsruhe Institute of Technology;

More information

Comment-based Multi-View Clustering of Web 2.0 Items

Comment-based Multi-View Clustering of Web 2.0 Items Comment-based Multi-View Clustering of Web 2.0 Items Xiangnan He 1 Min-Yen Kan 1 Peichu Xie 2 Xiao Chen 3 1 School of Computing, National University of Singapore 2 Department of Mathematics, National University

More information

Course Outline. Course Grading. Where to go for help. Academic Integrity. EE-589 Introduction to Neural Networks NN 1 EE

Course Outline. Course Grading. Where to go for help. Academic Integrity. EE-589 Introduction to Neural Networks NN 1 EE EE-589 Introduction to Neural Assistant Prof. Dr. Turgay IBRIKCI Room # 305 (322) 338 6868 / 139 Wensdays 9:00-12:00 Course Outline The course is divided in two parts: theory and practice. 1. Theory covers

More information

Robust Speech Recognition using DNN-HMM Acoustic Model Combining Noise-aware training with Spectral Subtraction

Robust Speech Recognition using DNN-HMM Acoustic Model Combining Noise-aware training with Spectral Subtraction INTERSPEECH 2015 Robust Speech Recognition using DNN-HMM Acoustic Model Combining Noise-aware training with Spectral Subtraction Akihiro Abe, Kazumasa Yamamoto, Seiichi Nakagawa Department of Computer

More information

Semi-supervised methods of text processing, and an application to medical concept extraction. Yacine Jernite Text-as-Data series September 17.

Semi-supervised methods of text processing, and an application to medical concept extraction. Yacine Jernite Text-as-Data series September 17. Semi-supervised methods of text processing, and an application to medical concept extraction Yacine Jernite Text-as-Data series September 17. 2015 What do we want from text? 1. Extract information 2. Link

More information

Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models

Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models Navdeep Jaitly 1, Vincent Vanhoucke 2, Geoffrey Hinton 1,2 1 University of Toronto 2 Google Inc. ndjaitly@cs.toronto.edu,

More information

Design Of An Automatic Speaker Recognition System Using MFCC, Vector Quantization And LBG Algorithm

Design Of An Automatic Speaker Recognition System Using MFCC, Vector Quantization And LBG Algorithm Design Of An Automatic Speaker Recognition System Using MFCC, Vector Quantization And LBG Algorithm Prof. Ch.Srinivasa Kumar Prof. and Head of department. Electronics and communication Nalanda Institute

More information

The Good Judgment Project: A large scale test of different methods of combining expert predictions

The Good Judgment Project: A large scale test of different methods of combining expert predictions The Good Judgment Project: A large scale test of different methods of combining expert predictions Lyle Ungar, Barb Mellors, Jon Baron, Phil Tetlock, Jaime Ramos, Sam Swift The University of Pennsylvania

More information

Speaker Identification by Comparison of Smart Methods. Abstract

Speaker Identification by Comparison of Smart Methods. Abstract Journal of mathematics and computer science 10 (2014), 61-71 Speaker Identification by Comparison of Smart Methods Ali Mahdavi Meimand Amin Asadi Majid Mohamadi Department of Electrical Department of Computer

More information

Speaker Recognition. Speaker Diarization and Identification

Speaker Recognition. Speaker Diarization and Identification Speaker Recognition Speaker Diarization and Identification A dissertation submitted to the University of Manchester for the degree of Master of Science in the Faculty of Engineering and Physical Sciences

More information

INVESTIGATION OF UNSUPERVISED ADAPTATION OF DNN ACOUSTIC MODELS WITH FILTER BANK INPUT

INVESTIGATION OF UNSUPERVISED ADAPTATION OF DNN ACOUSTIC MODELS WITH FILTER BANK INPUT INVESTIGATION OF UNSUPERVISED ADAPTATION OF DNN ACOUSTIC MODELS WITH FILTER BANK INPUT Takuya Yoshioka,, Anton Ragni, Mark J. F. Gales Cambridge University Engineering Department, Cambridge, UK NTT Communication

More information

A Survey on Unsupervised Machine Learning Algorithms for Automation, Classification and Maintenance

A Survey on Unsupervised Machine Learning Algorithms for Automation, Classification and Maintenance A Survey on Unsupervised Machine Learning Algorithms for Automation, Classification and Maintenance a Assistant Professor a epartment of Computer Science Memoona Khanum a Tahira Mahboob b b Assistant Professor

More information

Online Updating of Word Representations for Part-of-Speech Tagging

Online Updating of Word Representations for Part-of-Speech Tagging Online Updating of Word Representations for Part-of-Speech Tagging Wenpeng Yin LMU Munich wenpeng@cis.lmu.de Tobias Schnabel Cornell University tbs49@cornell.edu Hinrich Schütze LMU Munich inquiries@cislmu.org

More information

A Comparison of Two Text Representations for Sentiment Analysis

A Comparison of Two Text Representations for Sentiment Analysis 010 International Conference on Computer Application and System Modeling (ICCASM 010) A Comparison of Two Text Representations for Sentiment Analysis Jianxiong Wang School of Computer Science & Educational

More information

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should

More information

Speech Recognition by Indexing and Sequencing

Speech Recognition by Indexing and Sequencing International Journal of Computer Information Systems and Industrial Management Applications. ISSN 215-7988 Volume 4 (212) pp. 358 365 c MIR Labs, www.mirlabs.net/ijcisim/index.html Speech Recognition

More information

arxiv:submit/ [cs.cv] 2 Aug 2017

arxiv:submit/ [cs.cv] 2 Aug 2017 Associative Domain Adaptation Philip Haeusser 1,2 haeusser@in.tum.de Thomas Frerix 1 Alexander Mordvintsev 2 thomas.frerix@tum.de moralex@google.com 1 Dept. of Informatics, TU Munich 2 Google, Inc. Daniel

More information

Semi-Supervised Face Detection

Semi-Supervised Face Detection Semi-Supervised Face Detection Nicu Sebe, Ira Cohen 2, Thomas S. Huang 3, Theo Gevers Faculty of Science, University of Amsterdam, The Netherlands 2 HP Research Labs, USA 3 Beckman Institute, University

More information

Offline Writer Identification Using Convolutional Neural Network Activation Features

Offline Writer Identification Using Convolutional Neural Network Activation Features Pattern Recognition Lab Department Informatik Universität Erlangen-Nürnberg Prof. Dr.-Ing. habil. Andreas Maier Telefon: +49 9131 85 27775 Fax: +49 9131 303811 info@i5.cs.fau.de www5.cs.fau.de Offline

More information

AUTOMATIC DETECTION OF PROLONGED FRICATIVE PHONEMES WITH THE HIDDEN MARKOV MODELS APPROACH 1. INTRODUCTION

AUTOMATIC DETECTION OF PROLONGED FRICATIVE PHONEMES WITH THE HIDDEN MARKOV MODELS APPROACH 1. INTRODUCTION JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol. 11/2007, ISSN 1642-6037 Marek WIŚNIEWSKI *, Wiesława KUNISZYK-JÓŹKOWIAK *, Elżbieta SMOŁKA *, Waldemar SUSZYŃSKI * HMM, recognition, speech, disorders

More information

Distributed Learning of Multilingual DNN Feature Extractors using GPUs

Distributed Learning of Multilingual DNN Feature Extractors using GPUs Distributed Learning of Multilingual DNN Feature Extractors using GPUs Yajie Miao, Hao Zhang, Florian Metze Language Technologies Institute, School of Computer Science, Carnegie Mellon University Pittsburgh,

More information

PREDICTING SPEECH RECOGNITION CONFIDENCE USING DEEP LEARNING WITH WORD IDENTITY AND SCORE FEATURES

PREDICTING SPEECH RECOGNITION CONFIDENCE USING DEEP LEARNING WITH WORD IDENTITY AND SCORE FEATURES PREDICTING SPEECH RECOGNITION CONFIDENCE USING DEEP LEARNING WITH WORD IDENTITY AND SCORE FEATURES Po-Sen Huang, Kshitiz Kumar, Chaojun Liu, Yifan Gong, Li Deng Department of Electrical and Computer Engineering,

More information

Multi-Dimensional, Multi-Level, and Multi-Timepoint Item Response Modeling.

Multi-Dimensional, Multi-Level, and Multi-Timepoint Item Response Modeling. Multi-Dimensional, Multi-Level, and Multi-Timepoint Item Response Modeling. Bengt Muthén & Tihomir Asparouhov In van der Linden, W. J., Handbook of Item Response Theory. Volume One. Models, pp. 527-539.

More information

A Comparison of DHMM and DTW for Isolated Digits Recognition System of Arabic Language

A Comparison of DHMM and DTW for Isolated Digits Recognition System of Arabic Language A Comparison of DHMM and DTW for Isolated Digits Recognition System of Arabic Language Z.HACHKAR 1,3, A. FARCHI 2, B.MOUNIR 1, J. EL ABBADI 3 1 Ecole Supérieure de Technologie, Safi, Morocco. zhachkar2000@yahoo.fr.

More information

ScienceDirect. A Framework for Clustering Cardiac Patient s Records Using Unsupervised Learning Techniques

ScienceDirect. A Framework for Clustering Cardiac Patient s Records Using Unsupervised Learning Techniques Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 98 (2016 ) 368 373 The 6th International Conference on Current and Future Trends of Information and Communication Technologies

More information

CROSS-LANGUAGE INFORMATION RETRIEVAL USING PARAFAC2

CROSS-LANGUAGE INFORMATION RETRIEVAL USING PARAFAC2 1 CROSS-LANGUAGE INFORMATION RETRIEVAL USING PARAFAC2 Peter A. Chew, Brett W. Bader, Ahmed Abdelali Proceedings of the 13 th SIGKDD, 2007 Tiago Luís Outline 2 Cross-Language IR (CLIR) Latent Semantic Analysis

More information

System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks

System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks 1 Tzu-Hsuan Yang, 2 Tzu-Hsuan Tseng, and 3 Chia-Ping Chen Department of Computer Science and Engineering

More information

Class-Discriminative Weighted Distortion Measure for VQ-Based Speaker Identification

Class-Discriminative Weighted Distortion Measure for VQ-Based Speaker Identification Class-Discriminative Weighted Distortion Measure for VQ-Based Speaker Identification Tomi Kinnunen and Ismo Kärkkäinen University of Joensuu, Department of Computer Science, P.O. Box 111, 80101 JOENSUU,

More information

A new Dataset of Telephone-Based Human-Human Call-Center Interaction with Emotional Evaluation

A new Dataset of Telephone-Based Human-Human Call-Center Interaction with Emotional Evaluation A new Dataset of Telephone-Based Human-Human Call-Center Interaction with Emotional Evaluation Ingo Siegert 1, Kerstin Ohnemus 2 1 Cognitive Systems Group, Institute for Information Technology and Communications

More information

Evaluation of Teach For America:

Evaluation of Teach For America: EA15-536-2 Evaluation of Teach For America: 2014-2015 Department of Evaluation and Assessment Mike Miles Superintendent of Schools This page is intentionally left blank. ii Evaluation of Teach For America:

More information

Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling

Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling Notebook for PAN at CLEF 2013 Andrés Alfonso Caurcel Díaz 1 and José María Gómez Hidalgo 2 1 Universidad

More information

Human Emotion Recognition From Speech

Human Emotion Recognition From Speech RESEARCH ARTICLE OPEN ACCESS Human Emotion Recognition From Speech Miss. Aparna P. Wanare*, Prof. Shankar N. Dandare *(Department of Electronics & Telecommunication Engineering, Sant Gadge Baba Amravati

More information

Using Web Searches on Important Words to Create Background Sets for LSI Classification

Using Web Searches on Important Words to Create Background Sets for LSI Classification Using Web Searches on Important Words to Create Background Sets for LSI Classification Sarah Zelikovitz and Marina Kogan College of Staten Island of CUNY 2800 Victory Blvd Staten Island, NY 11314 Abstract

More information

Word Segmentation of Off-line Handwritten Documents

Word Segmentation of Off-line Handwritten Documents Word Segmentation of Off-line Handwritten Documents Chen Huang and Sargur N. Srihari {chuang5, srihari}@cedar.buffalo.edu Center of Excellence for Document Analysis and Recognition (CEDAR), Department

More information

Segregation of Unvoiced Speech from Nonspeech Interference

Segregation of Unvoiced Speech from Nonspeech Interference Technical Report OSU-CISRC-8/7-TR63 Department of Computer Science and Engineering The Ohio State University Columbus, OH 4321-1277 FTP site: ftp.cse.ohio-state.edu Login: anonymous Directory: pub/tech-report/27

More information

On the Formation of Phoneme Categories in DNN Acoustic Models

On the Formation of Phoneme Categories in DNN Acoustic Models On the Formation of Phoneme Categories in DNN Acoustic Models Tasha Nagamine Department of Electrical Engineering, Columbia University T. Nagamine Motivation Large performance gap between humans and state-

More information

arxiv: v1 [cs.cl] 2 Apr 2017

arxiv: v1 [cs.cl] 2 Apr 2017 Word-Alignment-Based Segment-Level Machine Translation Evaluation using Word Embeddings Junki Matsuo and Mamoru Komachi Graduate School of System Design, Tokyo Metropolitan University, Japan matsuo-junki@ed.tmu.ac.jp,

More information

CSL465/603 - Machine Learning

CSL465/603 - Machine Learning CSL465/603 - Machine Learning Fall 2016 Narayanan C Krishnan ckn@iitrpr.ac.in Introduction CSL465/603 - Machine Learning 1 Administrative Trivia Course Structure 3-0-2 Lecture Timings Monday 9.55-10.45am

More information

Lecture 9: Speech Recognition

Lecture 9: Speech Recognition EE E6820: Speech & Audio Processing & Recognition Lecture 9: Speech Recognition 1 Recognizing speech 2 Feature calculation Dan Ellis Michael Mandel 3 Sequence

More information

The NICT/ATR speech synthesis system for the Blizzard Challenge 2008

The NICT/ATR speech synthesis system for the Blizzard Challenge 2008 The NICT/ATR speech synthesis system for the Blizzard Challenge 2008 Ranniery Maia 1,2, Jinfu Ni 1,2, Shinsuke Sakai 1,2, Tomoki Toda 1,3, Keiichi Tokuda 1,4 Tohru Shimizu 1,2, Satoshi Nakamura 1,2 1 National

More information

Automatic Pronunciation Checker

Automatic Pronunciation Checker Institut für Technische Informatik und Kommunikationsnetze Eidgenössische Technische Hochschule Zürich Swiss Federal Institute of Technology Zurich Ecole polytechnique fédérale de Zurich Politecnico federale

More information

Using the Attribute Hierarchy Method to Make Diagnostic Inferences about Examinees Cognitive Skills in Algebra on the SAT

Using the Attribute Hierarchy Method to Make Diagnostic Inferences about Examinees Cognitive Skills in Algebra on the SAT The Journal of Technology, Learning, and Assessment Volume 6, Number 6 February 2008 Using the Attribute Hierarchy Method to Make Diagnostic Inferences about Examinees Cognitive Skills in Algebra on the

More information

Evaluating Interactive Visualization of Multidimensional Data Projection with Feature Transformation

Evaluating Interactive Visualization of Multidimensional Data Projection with Feature Transformation Multimodal Technologies and Interaction Article Evaluating Interactive Visualization of Multidimensional Data Projection with Feature Transformation Kai Xu 1, *,, Leishi Zhang 1,, Daniel Pérez 2,, Phong

More information

International Journal of Advanced Networking Applications (IJANA) ISSN No. :

International Journal of Advanced Networking Applications (IJANA) ISSN No. : International Journal of Advanced Networking Applications (IJANA) ISSN No. : 0975-0290 34 A Review on Dysarthric Speech Recognition Megha Rughani Department of Electronics and Communication, Marwadi Educational

More information

Malicious User Suppression for Cooperative Spectrum Sensing in Cognitive Radio Networks using Dixon s Outlier Detection Method

Malicious User Suppression for Cooperative Spectrum Sensing in Cognitive Radio Networks using Dixon s Outlier Detection Method Malicious User Suppression for Cooperative Spectrum Sensing in Cognitive Radio Networks using Dixon s Outlier Detection Method Sanket S. Kalamkar and Adrish Banerjee Department of Electrical Engineering

More information

POS tagging of Chinese Buddhist texts using Recurrent Neural Networks

POS tagging of Chinese Buddhist texts using Recurrent Neural Networks POS tagging of Chinese Buddhist texts using Recurrent Neural Networks Longlu Qin Department of East Asian Languages and Cultures longlu@stanford.edu Abstract Chinese POS tagging, as one of the most important

More information

SUPRA-SEGMENTAL FEATURE BASED SPEAKER TRAIT DETECTION

SUPRA-SEGMENTAL FEATURE BASED SPEAKER TRAIT DETECTION Odyssey 2014: The Speaker and Language Recognition Workshop 16-19 June 2014, Joensuu, Finland SUPRA-SEGMENTAL FEATURE BASED SPEAKER TRAIT DETECTION Gang Liu, John H.L. Hansen* Center for Robust Speech

More information

Twitter Sentiment Classification on Sanders Data using Hybrid Approach

Twitter Sentiment Classification on Sanders Data using Hybrid Approach IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 4, Ver. I (July Aug. 2015), PP 118-123 www.iosrjournals.org Twitter Sentiment Classification on Sanders

More information

Statewide Framework Document for:

Statewide Framework Document for: Statewide Framework Document for: 270301 Standards may be added to this document prior to submission, but may not be removed from the framework to meet state credit equivalency requirements. Performance

More information

Bridging Lexical Gaps between Queries and Questions on Large Online Q&A Collections with Compact Translation Models

Bridging Lexical Gaps between Queries and Questions on Large Online Q&A Collections with Compact Translation Models Bridging Lexical Gaps between Queries and Questions on Large Online Q&A Collections with Compact Translation Models Jung-Tae Lee and Sang-Bum Kim and Young-In Song and Hae-Chang Rim Dept. of Computer &

More information

A survey of multi-view machine learning

A survey of multi-view machine learning Noname manuscript No. (will be inserted by the editor) A survey of multi-view machine learning Shiliang Sun Received: date / Accepted: date Abstract Multi-view learning or learning with multiple distinct

More information

WE GAVE A LAWYER BASIC MATH SKILLS, AND YOU WON T BELIEVE WHAT HAPPENED NEXT

WE GAVE A LAWYER BASIC MATH SKILLS, AND YOU WON T BELIEVE WHAT HAPPENED NEXT WE GAVE A LAWYER BASIC MATH SKILLS, AND YOU WON T BELIEVE WHAT HAPPENED NEXT PRACTICAL APPLICATIONS OF RANDOM SAMPLING IN ediscovery By Matthew Verga, J.D. INTRODUCTION Anyone who spends ample time working

More information

OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS

OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS Václav Kocian, Eva Volná, Michal Janošek, Martin Kotyrba University of Ostrava Department of Informatics and Computers Dvořákova 7,

More information

UMass at TDT Similarity functions 1. BASIC SYSTEM Detection algorithms. set globally and apply to all clusters.

UMass at TDT Similarity functions 1. BASIC SYSTEM Detection algorithms. set globally and apply to all clusters. UMass at TDT James Allan, Victor Lavrenko, David Frey, and Vikas Khandelwal Center for Intelligent Information Retrieval Department of Computer Science University of Massachusetts Amherst, MA 3 We spent

More information

TRANSFER LEARNING IN MIR: SHARING LEARNED LATENT REPRESENTATIONS FOR MUSIC AUDIO CLASSIFICATION AND SIMILARITY

TRANSFER LEARNING IN MIR: SHARING LEARNED LATENT REPRESENTATIONS FOR MUSIC AUDIO CLASSIFICATION AND SIMILARITY TRANSFER LEARNING IN MIR: SHARING LEARNED LATENT REPRESENTATIONS FOR MUSIC AUDIO CLASSIFICATION AND SIMILARITY Philippe Hamel, Matthew E. P. Davies, Kazuyoshi Yoshii and Masataka Goto National Institute

More information

Lip reading: Japanese vowel recognition by tracking temporal changes of lip shape

Lip reading: Japanese vowel recognition by tracking temporal changes of lip shape Lip reading: Japanese vowel recognition by tracking temporal changes of lip shape Koshi Odagiri 1, and Yoichi Muraoka 1 1 Graduate School of Fundamental/Computer Science and Engineering, Waseda University,

More information

Large-Scale Web Page Classification. Sathi T Marath. Submitted in partial fulfilment of the requirements. for the degree of Doctor of Philosophy

Large-Scale Web Page Classification. Sathi T Marath. Submitted in partial fulfilment of the requirements. for the degree of Doctor of Philosophy Large-Scale Web Page Classification by Sathi T Marath Submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy at Dalhousie University Halifax, Nova Scotia November 2010

More information