MAP Based Speaker Adaptation in Very Large Vocabulary Speech Recognition of Czech
|
|
- Griselda McDonald
- 5 years ago
- Views:
Transcription
1 42 P. ČERVA, J. NOUZA, MAP BASED SPEAKER ADAPAION IN VERY LARGE VOCABULARY SPEECH RECOGNIION MAP Based Speaker Adapaion in Very Large Vocabulary Speech Recogniion of Czech Per ČERVA, Jan NOUZA Dep. of Elecronics and Signal Processing, echnical Universiy of Liberec, Hálkova 6, 46 7 Liberec, Czech Republic per.cerva@vslib.cz, jan.nouza@vslib.cz Absrac. he paper deals wih he problem of efficien adapaion of speech recogniion sysems o individual users. he goal is o achieve beer performance in specific applicaions where one known speaker is expeced. In our approach we adop he MAP (Maximum A Poseriori) mehod for his purpose. he MAP based formulae for he adapaion of he HMM (Hidden Markov Model) parameers are described. Several alernaive versions of his mehod have been implemened and experimenally verified in wo areas, firs in he isolaed-word recogniion (IWR) ask and laer also in he large vocabulary coninuous speech recogniion (LVCSR) sysem, boh developed for he Czech language. he resuls show ha he word error rae (WER) can be reduced by more han 2% for a speaker who provides ens of words (in case of IWR) or ens of senences (in case of LVCSR) for he adapaion. Recenly, we have used he described mehods in he design of wo pracical applicaions: voice dicaion o a PC and auomaic ranscripion of radio and V news. Keywords Speech recogniion, speaker adapaion, maximum a poseriori mehod, hidden Markov models.. Inroducion Modern sysems for auomaic speech recogniion (ASR) are based on he echnique ha uses hidden Markov models (HMM), usually wih coninuous densiy funcion (CDHMM). Saisical parameers of hese probabilisic models mus be esimaed in he phase of he sysem raining by exploiing large daabases of speech recordings. In many pracical applicaions, ASR sysems are used by speakers whose speaking characerisics are differen, depending on heir gender, dialec, ec. o make he ASR robus agains all hese variaions and o allow almos everybody o use he sysems, hese mus operae as speaker independen - SI. For he raining of a successful SI sysem, several ens of hours of annoaed speech recorded by hundreds of differen persons are necessary. Bu here are applicaions, le e.g. voice conrol of a PC or voice dicaion, where only one person is expeced o use hem. In such a case, he HMMs could be rained on he user s speech only and hen he ASR would operae as speaker dependen - SD. Unforunaely, even in his specific case he user would be required o record a leas several hours of his/her speech for raining purposes. Generally his is hardly feasible for he user as well as for he provider of he sysem. A more accepable soluion consiss in he adapaion of he exising SI models for he given speaker and hus making he sysem speaker adaped - SA. he major advanage of his approach is ha he speaker will be asked o record a significanly smaller amoun of daa (less han hour). his recording and reraining is usually done on he user s own compuer, which means ha he ASR sysem also adaps is parameers o he characerisics of he given microphone and o specific noise of he environmen in which he speaker alks. ha is why adapaion mehods are so imporan for pracice and many researchers are sill working on echniques ha lead o furher improvemen in recogniion while asking less adapaion daa from he user. In his paper we describe he mehod ha uilizes he Maximum A Poseriori (MAP) sraegy o he adapaion of phonemically based isolaed-word recogniion (IWR) and coninuous-speech recogniion (CSR) sysems. Boh have been developed for he Czech language and can operae wih very large vocabularies (above, words). his paper is srucured as follows: In he nex secion we describe he heoreical background of he MAP based speaker adapaion echnique. he resuls from quie exensive experimenal evaluaion are presened in secion 3 (for he IWR as and secion 4 (for he CSR as. 2. MAP Based Speaker Adapaion he adapaion (esimaion) mehod MAP (Maximum A Poseriori) [] is based on a differen sraegy han he Maximum Lelihood Esimaion (MLE) [2] ha is widely used during he sandard HMM raining. While he MLE assumes he model parameers o be unknown bu fixed, he MAP assumes hese parameers o be random variables wih known prior disribuions. his informaion abou prior disribuions compensaes he lack of adapaion daa during he adapaion process.
2 RADIOENGINEERING, VOL. 3, NO. 3, SEPEMBER he erm ζ ( i, in (2.3) represens he occupaion lelihood of k-h mixure componen of sae i and indicaes he amoun of he daa used for he adapaion of his mixure. Hence he new esimaes of he SA parameers have form of weighed sum where he original SI parameers are weighed by facor Fig.. Illusraion of he MAP based adapaion process. Le X = {x, x 2,, x } be he sequence of parameerized vecors of adapaion daa and Φ) he prior disribuion of parameer Φ. he parameer Φ can represen means, variances or weighing coefficien of one Gaussian componen (mixure) of he given HMM. he opimal value of parameer Φaccording o MAP esimae can be expressed as Φ MAP = arg max Φ X), (2.) Φ τ / τ + ζ ( i, and mixed wih hose esimaed from he adapaion daa by he MLE echnique. his procedure is illusraed in Fig.. When increasing he amoun of he adapaion daa lim ζ ( i, and he MAP esimae converges o he SD model which would resul from he MLE mehod. he formulae for he MAP esimaion of he variances and mixure weighs can be derived in a similar way. he expressions are raher complex and can be found in [3]. where Φ X) is he poserior probabiliy densiy disribuion of parameer Φ. According o Bayes rule Φ X) can be expressed as 3. Experimenal Evaluaion in IWR ask X Φ) Φ) Φ X) =. (2.2) he evaluaion was performed using he ASR sysem X) developed in our lab [5]. A he momen his is he mos he maximizaion of Φ X) is hen reached by changing powerful IWR sysem designed for Czech. Is sandard he value of Φ o maximize X Φ) Φ). vocabulary conains 8, mos frequen words. he sysem uses hree-sae CDHMMs of Czech phonemes [4] he criical quesion in he MAP based speaker adapaion is: how o deermine he prior parameers. In he pensaed by higher numbers of mixures (32, 64 or even and noises. hese are conex independen, which is com- sandard MAP mehod hese parameers are aken direcly ). he feaure vecor includes 39 MFCC parameers (3 from he available SI models. In secion 4.3 we will show saic coefficiens ogeher wih heir firs and second derivaives) calculaed from he signal sampled a 8 khz rae ha beer resuls can be achieved if gender dependen SI models are used. ino 6 bis. For CDHMMs wih Gaussian sae observaion densiies, where λ = ( µ, Σ i is he k-h Gaussian componen of sae i wih mixure weigh c k, he soluion of he MAP esimaion for he means is: ζ ( i, SA τ SI µ = µ + µ ˆ τ + ζ ( i, τ + ζ ( i,. (2.3) Here τ is he weighing facor (a free parameer), µ SI is he mean vecor of k-h mixure componen of sae i of he given SI model, µ SA is he adaped mean vecor and µ^ is he ML esimae of his vecor compued by Baum-Welch algorihm [2] from all he adapaion daa. In he firs series of experimens, we sudied he influence of he amoun of he adapaion daa on he recogniion accuracy of he adaped sysem (see ab. and Fig.2). Here, only he Gaussian means were adaped using consan value τ = 5 for all he mixures. his value was found opimal in preliminary ess. he se of 43 Czech randomly chosen words was available for he adapaion, from which subses wih variable size were used in he adapaion procedure implemened in accord wih he eq. (2.3). Anoher se of 862 words from he same speaker was used for he ess. he ess were performed wih wo speakers and wo sysem seings (32 or 64 mixures) and he average resuls are given in ab..
3 44 P. ČERVA, J. NOUZA, MAP BASED SPEAKER ADAPAION IN VERY LARGE VOCABULARY SPEECH RECOGNIION number of adapaion words rae (SI) sysem wih 32 mixures rae sysem wih 64 mixures recogniion rae ab.. Recogniion accuracy as funcion of he number of adapaion words in IWR ask mixures 64 mixures number of adapaion words Fig. 2. Graph of he recogniion accuracy as funcion of he number of adapaion words in IWR ask. We can noice ha even if only words are used for he adapaion, he relaive word error rae reducion () is abou 2 %. When words are used he reaches 35 %. Wih more adapaion daa he improvemen ges slower. In anoher series of experimens, we sudied he possibiliy o adap also Gaussian variances and mixure weighs. he resuls showed ha he adapaion of hese parameers had only a negligible effec on he recogniion accuracy (see also secion 4.2). We also compared wo ypes of adapaion daa isolaed words vs. coninuously spoken uerances and noiced ha he former were more appropriae for he IWR ask, mos probably because of he voice sress which occurs a iniial pars of isolaed words. 4. Experimenal Evaluaion in CSR ask In his case we used our own large vocabulary CSR sysem [7]. Is vocabulary was made of he 3, mos frequen words. he acousic par of he sysem was same as for he IWR ask (64-mixure HMMs). he linguisic 35 4 par was based on he bigram language model esimaed on a corpus conaining abou 2 GB of Czech (mainly newspaper) ex. Because he ess were ime consuming, he iniial experimens were performed only for one speaker. Afer finding he appropriae values of he free adapaion parameers we run anoher series of ess in which more speakers were involved. 4. Experimens Performed for One Speaker Again, in he firs series of experimens we sudied he impac of he amoun of he adapaion daa here measured by he number of adapaion senences. he es se conained 462 senences recorded by he same speaker. here were 74 words in hese es uerances, from which 225 were no presen in he vocabulary, i.e. he Ouof-vocabulary (OOV) rae was 3.7 %. A varying number of oher o 6 senences were used for he adapaion. he resuls are summarized in ab. 2 and Fig. 3. number of adapaion senences lengh [min] rae (SI) (SD) recogniion rae ab. 2. Recogniion rae as funcion of he number of adapaion senences for he LVCSR sysem SA model SD model number of senences for adapaion Fig. 3. Graph of he dependency of he recogniion accuracy on he number of adapaion senences for he LVCSR sysem wih 64-mixure HMMs. For comparison we creaed also SD models by using he oal number of 955 senences recorded by he same speaker. In Fig. 3 we can see he convergence of he SA sysem o he SD one as he amoun of he adapaion daa increases. he very imporan fac is ha senences used in adapaion resuled in 8%. In secion 4.2 his significan improvemen is verified for more speakers. In he second series of experimens (see ab. 3 and Fig. 4) we sudied he impac of weighing coefficien τ
4 RADIOENGINEERING, VOL. 3, NO. 3, SEPEMBER on he recogniion rae. Furhermore, also Gaussian variances and mixure weighs were adaped in he same manner. Here, he adapaion se was fixed o senences. weigh τ recogniion rae adapaion of means adapaion of means and weighs of mixures Adapaion of means, weighs of mixures and variances ab. 3. Resuls of MAP based adapaion for differen values of adapaion weigh and for differen modes of adapaion. from radio news, he laer using sandard microphone conneced o a PC. he srucure of his es daabase is shown in ab. 4. In oal here were 566 senences (more han 2, words) available for he esing and senences per speaker for he adapaion of HMM means. he weighing facor τ was se o. gender of he speaker Microphone/ Radio lengh of adap. senences [min] SI sysem SA sysem M M M F F F F M M M R R M R means means + weighs means + weighs + variances (SA agains SI) ab. 4. Resuls of MAP based adapaion of means for differen speakers by using adapaion senences from each. ab. 4 proves ha he adapaion was successful for all he speakers. he average reducion of word error rae (he value of ) was 2.8 % weigh Fig. 4. Resuls of MAP based adapaion for differen values of adapaion weigh and for differen modes of adapaion. he resuls show ha values vary from 7.8 % o 2. % when he weighing coefficien τ changes in range from o 2 and when only means are adaped. his demonsraes he imporance of he proper value of τ on he resuls of he adapaion. he experimen also proves ha he exension of MAP based speaker adapaion from he means only o he complee HMM parameers brings only a small absolue improvemen in he (abou.6 % in average). he opimal value of τ depends on he amoun of he adapaion daa (see [3]) as well as on he ype of adaped parameers. From ab. 3 we can noice ha for he given speaker he opimal value is close o in case adapaion senences are used. 4.2 Experimens Performed for More Speakers he recordings from seven oher speakers were used in his evaluaion. he people were eiher professional (broadcas) speakers or sudens. he former were recorded Applicaion of Gender Dependen Models for Speaker Adapaion I is known ha male and female voices are differen. his fac can be used also in speaker adapaion. Hence we performed anoher series of experimens wih he same speakers as above and GD (gender dependen) models. SI models GD models rae rae ab. 5. Speaker recogniion raes achieved for 7 speakers and eiher general SI models or GD (gender dependen) ones. he simples adapaion echnique only consised in using gender dependen models rained eiher on male or female subses of he general raining daabase. As shown in ab. 5 his simple approach led o small bu consisen improvemen (average was 7.7 %). In he second experimen he parameers of he GD models were used as priors for he MAP based adapaion of he means, i.e. he GD models raher han he SI ones were adaped. From ab. 6 we can observe ha using he prior GD models in he adapaion yielded slighly beer resuls in mos cases.
5 46 P. ČERVA, J. NOUZA, MAP BASED SPEAKER ADAPAION IN VERY LARGE VOCABULARY SPEECH RECOGNIION prior parameers SI models GD models Acknowledgmens his work has been parly suppored by he Gran Agency of he Czech Republic (gran no. 2/2/24) and hrough research goal projec MSM ab. 6. Resuls of speaker adapaion performed by using GD and SI models as priors for he MAP based adapaion. 5. Conclusion his paper deals wih he problem of he efficien speaker adapaion for he very large vocabulary speech recogniion of Czech. We focus on he MAP based mehods whose heoreical background is briefly described. We applied his echnique wih several differen variaions, namely for Gaussian means, variances and mixure weighs, using eiher gender independen or gender dependen models as prior informaion. Our resuls from exensive experimens demonsrae ha he word error rae (WER) in isolaed-word recogniion can be reduced by 2 % or even 35 % if he speaker provides or adapaion words, respecively. In he more complex ask of coninuous speech recogniion, he WER will be reduced by abou 2 % when senences are used for adapaion. Similar resuls have been repored also in [7], where hey were achieved by commercial sofware (HK). Our implemenaion has several advanages: I is more compac and flexible, because i allows furher modificaion and opimizaion. One of hem is he exension owards he adapaion of variances and mixure weighs. In such case he was furher increased in average abou.6 % in he ask of LVCSR. Addiional enhancemen (abou %) was achieved by exploiing gender dependen models as priors wihin he MAP reesimaion. Recenly, all he described mehods are applied in he pracical design of he sysems for voice dicaion ino a PC. One version should serve as a Czech IWR dicaion sysem working wih 5,-word vocabulary, he oher as a CSR dicaion machine wih a 5, word lexicon. hanks o he adapaion ogeher wih furher improvemen of lexical and language model, he recogniion rae will ge above he 9 % level. Anoher benefi from he research can be expeced in he design of he sysem for he auomaic ranscripion of broadcas news. he use of he special models adaped o he speech of key-speakers will improve he overall accuracy of he ranscripion. References: [] GAUVAIN, J.L., LEE, C.H. Maximum a poseriori esimaion for mulivariae Gaussian mixure observaions of Markov chains. IEEE rans. SAP. 994, vol. 2, p [2] HUANG, X.D., ACERO, A., HON, H.W. Spoken language processing. Englewood Cliffs: Prenice Hall, 2. [3] ČERVA, P. Mehods of speaker adapaion for speech recogniion sysem. Diploma hesis (in Czech). U of Liberec. 24. [4] NOUZA, J., PSUKA, J., UHLÍŘ, J. Phoneic alphabe for speech recogniion of Czech. Radioengineering. 997, vol. 6, no. 4, p.6 o 2. [5] NOUZA, J., NOUZA,. A Voice dicaion sysem for a millionword Czech vocabulary. Proc. of Conference on Compuing, Communicaion and Conrol echnologies. Ausin, 24. [6] NOUZA, J., NEJEDLOVA, D., ZDANSKY, J., KOLORENC, J. Very large vocabulary speech recogniion sysem for auomaic ranscripion of Czech broadcas programs. Proc. of In. Conference on Spoken Language Processing (ISCLP 4). Jeju, 24. [7] ŽELEZNÝ, M. Speaker adapaion in coninuous speech recogniion sysem of Czech. PhD hesis (in Czech). ZČU Plzeň 2. Abou Auhors... Jan NOUZA (957) received maser degree (98) and docor degree (986) in elecommunicaions a he Czech echnical Universiy (Faculy of Elecrical Engineering) in Prague. Since 986 he has been a he echnical Universiy of Liberec (UL). In 999 he became professor a he Dep. of Elecronics and Signal Processing. His major research field is speech ineracion beween human and compuer wih he special focus on speech recogniion. He is he head of he Speech Processing Laboraory (Speech- Lab) a he UL founded by him in 993. He is a IEEE member (Signal Processing Sociey) and a member of he Inernaional Speech Communicaion Associaion (ISCA). Per ČERVA was born in Liberec in 98. In 24 he received maser degree a he echnical Universiy of Liberec (UL) and joined he SpeechLab eam as a PhD suden. His research work is focused on speaker adapaion and speech recogniion of Czech.
Neural Network Model of the Backpropagation Algorithm
Neural Nework Model of he Backpropagaion Algorihm Rudolf Jakša Deparmen of Cyberneics and Arificial Inelligence Technical Universiy of Košice Lená 9, 4 Košice Slovakia jaksa@neuron.uke.sk Miroslav Karák
More informationChannel Mapping using Bidirectional Long Short-Term Memory for Dereverberation in Hands-Free Voice Controlled Devices
Z. Zhang e al.: Channel Mapping using Bidirecional Long Shor-Term Memory for Dereverberaion in Hands-Free Voice Conrolled Devices 525 Channel Mapping using Bidirecional Long Shor-Term Memory for Dereverberaion
More informationAn Effiecient Approach for Resource Auto-Scaling in Cloud Environments
Inernaional Journal of Elecrical and Compuer Engineering (IJECE) Vol. 6, No. 5, Ocober 2016, pp. 2415~2424 ISSN: 2088-8708, DOI: 10.11591/ijece.v6i5.10639 2415 An Effiecien Approach for Resource Auo-Scaling
More informationFast Multi-task Learning for Query Spelling Correction
Fas Muli-ask Learning for Query Spelling Correcion Xu Sun Dep. of Saisical Science Cornell Universiy Ihaca, NY 14853 xusun@cornell.edu Anshumali Shrivasava Dep. of Compuer Science Cornell Universiy Ihaca,
More informationMyLab & Mastering Business
MyLab & Masering Business Efficacy Repor 2013 MyLab & Masering: Business Efficacy Repor 2013 Edied by Michelle D. Speckler 2013 Pearson MyAccouningLab, MyEconLab, MyFinanceLab, MyMarkeingLab, and MyOMLab
More informationMore Accurate Question Answering on Freebase
More Accurae Quesion Answering on Freebase Hannah Bas, Elmar Haussmann Deparmen of Compuer Science Universiy of Freiburg 79110 Freiburg, Germany {bas, haussmann}@informaik.uni-freiburg.de ABSTRACT Real-world
More information1 Language universals
AS LX 500 Topics: Language Uniersals Fall 2010, Sepember 21 4a. Anisymmery 1 Language uniersals Subjec-erb agreemen and order Bach (1971) discusses wh-quesions across SO and SO languages, hypohesizing:...
More informationInformation Propagation for informing Special Population Subgroups about New Ground Transportation Services at Airports
Downloaded from ascelibrary.org by Basil Sephanis on 07/13/16. Copyrigh ASCE. For personal use only; all righs reserved. Informaion Propagaion for informing Special Populaion Subgroups abou New Ground
More informationSemi-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 informationLearning 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 informationModeling 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 informationModeling 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 informationBAUM-WELCH TRAINING FOR SEGMENT-BASED SPEECH RECOGNITION. Han Shu, I. Lee Hetherington, and James Glass
BAUM-WELCH TRAINING FOR SEGMENT-BASED SPEECH RECOGNITION Han Shu, I. Lee Hetherington, and James Glass Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge,
More informationA 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 informationUnvoiced Landmark Detection for Segment-based Mandarin Continuous Speech Recognition
Unvoiced Landmark Detection for Segment-based Mandarin Continuous Speech Recognition Hua Zhang, Yun Tang, Wenju Liu and Bo Xu National Laboratory of Pattern Recognition Institute of Automation, Chinese
More informationLikelihood-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 informationSpeech 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 informationSegmental Conditional Random Fields with Deep Neural Networks as Acoustic Models for First-Pass Word Recognition
Segmental Conditional Random Fields with Deep Neural Networks as Acoustic Models for First-Pass Word Recognition Yanzhang He, Eric Fosler-Lussier Department of Computer Science and Engineering The hio
More informationA 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 informationA 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 informationRobust 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 informationINVESTIGATION 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 informationAn Online Handwriting Recognition System For Turkish
An Online Handwriting Recognition System For Turkish Esra Vural, Hakan Erdogan, Kemal Oflazer, Berrin Yanikoglu Sabanci University, Tuzla, Istanbul, Turkey 34956 ABSTRACT Despite recent developments in
More informationAutoregressive 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 informationMalicious 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 information2/15/13. POS Tagging Problem. Part-of-Speech Tagging. Example English Part-of-Speech Tagsets. More Details of the Problem. Typical Problem Cases
POS Tagging Problem Part-of-Speech Tagging L545 Spring 203 Given a sentence W Wn and a tagset of lexical categories, find the most likely tag T..Tn for each word in the sentence Example Secretariat/P is/vbz
More informationInvestigation 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 informationSpeech 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 informationA 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 informationWHEN 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 informationBUILDING 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 informationNatural Language Processing. George Konidaris
Natural Language Processing George Konidaris gdk@cs.brown.edu Fall 2017 Natural Language Processing Understanding spoken/written sentences in a natural language. Major area of research in AI. Why? Humans
More informationErkki Mäkinen State change languages as homomorphic images of Szilard languages
Erkki Mäkinen State change languages as homomorphic images of Szilard languages UNIVERSITY OF TAMPERE SCHOOL OF INFORMATION SCIENCES REPORTS IN INFORMATION SCIENCES 48 TAMPERE 2016 UNIVERSITY OF TAMPERE
More informationMULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Ch 2 Test Remediation Work Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Provide an appropriate response. 1) High temperatures in a certain
More informationFrom Empire to Twenty-First Century Britain: Economic and Political Development of Great Britain in the 19th and 20th Centuries 5HD391
Provisional list of courses for Exchange students Fall semester 2017: University of Economics, Prague Courses stated below are offered by particular departments and faculties at the University of Economics,
More informationPREDICTING 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 informationNon 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 informationADVANCES 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 informationEli Yamamoto, Satoshi Nakamura, Kiyohiro Shikano. Graduate School of Information Science, Nara Institute of Science & Technology
ISCA Archive SUBJECTIVE EVALUATION FOR HMM-BASED SPEECH-TO-LIP MOVEMENT SYNTHESIS Eli Yamamoto, Satoshi Nakamura, Kiyohiro Shikano Graduate School of Information Science, Nara Institute of Science & Technology
More informationInternational 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 informationA General Class of Noncontext Free Grammars Generating Context Free Languages
INFORMATION AND CONTROL 43, 187-194 (1979) A General Class of Noncontext Free Grammars Generating Context Free Languages SARWAN K. AGGARWAL Boeing Wichita Company, Wichita, Kansas 67210 AND JAMES A. HEINEN
More informationCS 598 Natural Language Processing
CS 598 Natural Language Processing Natural language is everywhere Natural language is everywhere Natural language is everywhere Natural language is everywhere!"#$%&'&()*+,-./012 34*5665756638/9:;< =>?@ABCDEFGHIJ5KL@
More informationAUTOMATIC 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 informationTravis Park, Assoc Prof, Cornell University Donna Pearson, Assoc Prof, University of Louisville. NACTEI National Conference Portland, OR May 16, 2012
Travis Park, Assoc Prof, Cornell University Donna Pearson, Assoc Prof, University of Louisville NACTEI National Conference Portland, OR May 16, 2012 NRCCTE Partners Four Main Ac5vi5es Research (Scientifically-based)!!
More informationBENCHMARKING OF FREE AUTHORING TOOLS FOR MULTIMEDIA COURSES DEVELOPMENT
36 Acta Electrotechnica et Informatica, Vol. 11, No. 3, 2011, 36 41, DOI: 10.2478/v10198-011-0033-8 BENCHMARKING OF FREE AUTHORING TOOLS FOR MULTIMEDIA COURSES DEVELOPMENT Peter KOŠČ *, Mária GAMCOVÁ **,
More informationWord 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 informationMandarin Lexical Tone Recognition: The Gating Paradigm
Kansas Working Papers in Linguistics, Vol. 0 (008), p. 8 Abstract Mandarin Lexical Tone Recognition: The Gating Paradigm Yuwen Lai and Jie Zhang University of Kansas Research on spoken word recognition
More informationDNN 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 informationSoftprop: Softmax Neural Network Backpropagation Learning
Softprop: Softmax Neural Networ Bacpropagation Learning Michael Rimer Computer Science Department Brigham Young University Provo, UT 84602, USA E-mail: mrimer@axon.cs.byu.edu Tony Martinez Computer Science
More informationSTUDIES WITH FABRICATED SWITCHBOARD DATA: EXPLORING SOURCES OF MODEL-DATA MISMATCH
STUDIES WITH FABRICATED SWITCHBOARD DATA: EXPLORING SOURCES OF MODEL-DATA MISMATCH Don McAllaster, Larry Gillick, Francesco Scattone, Mike Newman Dragon Systems, Inc. 320 Nevada Street Newton, MA 02160
More informationLEARNING A SEMANTIC PARSER FROM SPOKEN UTTERANCES. Judith Gaspers and Philipp Cimiano
LEARNING A SEMANTIC PARSER FROM SPOKEN UTTERANCES Judith Gaspers and Philipp Cimiano Semantic Computing Group, CITEC, Bielefeld University {jgaspers cimiano}@cit-ec.uni-bielefeld.de ABSTRACT Semantic parsers
More informationCourses below are sorted by the column Field of study for your better orientation. The list is subject to change.
Provisional list of courses for Exchange students Spring semester 2017: University of Economics, Prague Courses stated below are offered by particular departments and faculties at the University of Economics,
More informationA simulated annealing and hill-climbing algorithm for the traveling tournament problem
European Journal of Operational Research xxx (2005) xxx xxx Discrete Optimization A simulated annealing and hill-climbing algorithm for the traveling tournament problem A. Lim a, B. Rodrigues b, *, X.
More informationLecture 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 informationSEMI-SUPERVISED ENSEMBLE DNN ACOUSTIC MODEL TRAINING
SEMI-SUPERVISED ENSEMBLE DNN ACOUSTIC MODEL TRAINING Sheng Li 1, Xugang Lu 2, Shinsuke Sakai 1, Masato Mimura 1 and Tatsuya Kawahara 1 1 School of Informatics, Kyoto University, Sakyo-ku, Kyoto 606-8501,
More informationCS Machine Learning
CS 478 - Machine Learning Projects Data Representation Basic testing and evaluation schemes CS 478 Data and Testing 1 Programming Issues l Program in any platform you want l Realize that you will be doing
More informationThe Impact of Formative Assessment and Remedial Teaching on EFL Learners Listening Comprehension N A H I D Z A R E I N A S TA R A N YA S A M I
The Impact of Formative Assessment and Remedial Teaching on EFL Learners Listening Comprehension N A H I D Z A R E I N A S TA R A N YA S A M I Formative Assessment The process of seeking and interpreting
More informationMulti-Lingual Text Leveling
Multi-Lingual Text Leveling Salim Roukos, Jerome Quin, and Todd Ward IBM T. J. Watson Research Center, Yorktown Heights, NY 10598 {roukos,jlquinn,tward}@us.ibm.com Abstract. Determining the language proficiency
More informationDetecting English-French Cognates Using Orthographic Edit Distance
Detecting English-French Cognates Using Orthographic Edit Distance Qiongkai Xu 1,2, Albert Chen 1, Chang i 1 1 The Australian National University, College of Engineering and Computer Science 2 National
More informationReinforcement Learning by Comparing Immediate Reward
Reinforcement Learning by Comparing Immediate Reward Punit Pandey DeepshikhaPandey Dr. Shishir Kumar Abstract This paper introduces an approach to Reinforcement Learning Algorithm by comparing their immediate
More informationE-learning Strategies to Support Databases Courses: a Case Study
E-learning Strategies to Support Databases Courses: a Case Study Luisa M. Regueras 1, Elena Verdú 1, María J. Verdú 1, María Á. Pérez 1, and Juan P. de Castro 1 1 University of Valladolid, School of Telecommunications
More informationHuman 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 informationAP Statistics Summer Assignment 17-18
AP Statistics Summer Assignment 17-18 Welcome to AP Statistics. This course will be unlike any other math class you have ever taken before! Before taking this course you will need to be competent in basic
More informationListening and Speaking Skills of English Language of Adolescents of Government and Private Schools
Listening and Speaking Skills of English Language of Adolescents of Government and Private Schools Dr. Amardeep Kaur Professor, Babe Ke College of Education, Mudki, Ferozepur, Punjab Abstract The present
More informationThe 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 informationIEEE 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 informationCorrective Feedback and Persistent Learning for Information Extraction
Corrective Feedback and Persistent Learning for Information Extraction Aron Culotta a, Trausti Kristjansson b, Andrew McCallum a, Paul Viola c a Dept. of Computer Science, University of Massachusetts,
More informationEvolutive Neural Net Fuzzy Filtering: Basic Description
Journal of Intelligent Learning Systems and Applications, 2010, 2: 12-18 doi:10.4236/jilsa.2010.21002 Published Online February 2010 (http://www.scirp.org/journal/jilsa) Evolutive Neural Net Fuzzy Filtering:
More informationDIRECT ADAPTATION OF HYBRID DNN/HMM MODEL FOR FAST SPEAKER ADAPTATION IN LVCSR BASED ON SPEAKER CODE
2014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP) DIRECT ADAPTATION OF HYBRID DNN/HMM MODEL FOR FAST SPEAKER ADAPTATION IN LVCSR BASED ON SPEAKER CODE Shaofei Xue 1
More informationGCE. Mathematics (MEI) Mark Scheme for June Advanced Subsidiary GCE Unit 4766: Statistics 1. Oxford Cambridge and RSA Examinations
GCE Mathematics (MEI) Advanced Subsidiary GCE Unit 4766: Statistics 1 Mark Scheme for June 2013 Oxford Cambridge and RSA Examinations OCR (Oxford Cambridge and RSA) is a leading UK awarding body, providing
More informationUNIDIRECTIONAL LONG SHORT-TERM MEMORY RECURRENT NEURAL NETWORK WITH RECURRENT OUTPUT LAYER FOR LOW-LATENCY SPEECH SYNTHESIS. Heiga Zen, Haşim Sak
UNIDIRECTIONAL LONG SHORT-TERM MEMORY RECURRENT NEURAL NETWORK WITH RECURRENT OUTPUT LAYER FOR LOW-LATENCY SPEECH SYNTHESIS Heiga Zen, Haşim Sak Google fheigazen,hasimg@google.com ABSTRACT Long short-term
More informationVimala.C Project Fellow, Department of Computer Science Avinashilingam Institute for Home Science and Higher Education and Women Coimbatore, India
World of Computer Science and Information Technology Journal (WCSIT) ISSN: 2221-0741 Vol. 2, No. 1, 1-7, 2012 A Review on Challenges and Approaches Vimala.C Project Fellow, Department of Computer Science
More informationBody-Conducted Speech Recognition and its Application to Speech Support System
Body-Conducted Speech Recognition and its Application to Speech Support System 4 Shunsuke Ishimitsu Hiroshima City University Japan 1. Introduction In recent years, speech recognition systems have been
More informationSpeech Synthesis in Noisy Environment by Enhancing Strength of Excitation and Formant Prominence
INTERSPEECH September,, San Francisco, USA Speech Synthesis in Noisy Environment by Enhancing Strength of Excitation and Formant Prominence Bidisha Sharma and S. R. Mahadeva Prasanna Department of Electronics
More informationClass-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 informationEdinburgh 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 informationArabic Orthography vs. Arabic OCR
Arabic Orthography vs. Arabic OCR Rich Heritage Challenging A Much Needed Technology Mohamed Attia Having consistently been spoken since more than 2000 years and on, Arabic is doubtlessly the oldest among
More informationSpeech Recognition using Acoustic Landmarks and Binary Phonetic Feature Classifiers
Speech Recognition using Acoustic Landmarks and Binary Phonetic Feature Classifiers October 31, 2003 Amit Juneja Department of Electrical and Computer Engineering University of Maryland, College Park,
More informationTruth Inference in Crowdsourcing: Is the Problem Solved?
Truth Inference in Crowdsourcing: Is the Problem Solved? Yudian Zheng, Guoliang Li #, Yuanbing Li #, Caihua Shan, Reynold Cheng # Department of Computer Science, Tsinghua University Department of Computer
More informationLevel 1 Mathematics and Statistics, 2015
91037 910370 1SUPERVISOR S Level 1 Mathematics and Statistics, 2015 91037 Demonstrate understanding of chance and data 9.30 a.m. Monday 9 November 2015 Credits: Four Achievement Achievement with Merit
More informationLecture 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 informationMulti-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 informationDeep Neural Network Language Models
Deep Neural Network Language Models Ebru Arısoy, Tara N. Sainath, Brian Kingsbury, Bhuvana Ramabhadran IBM T.J. Watson Research Center Yorktown Heights, NY, 10598, USA {earisoy, tsainath, bedk, bhuvana}@us.ibm.com
More informationWeb as Corpus. Corpus Linguistics. Web as Corpus 1 / 1. Corpus Linguistics. Web as Corpus. web.pl 3 / 1. Sketch Engine. Corpus Linguistics
(L615) Markus Dickinson Department of Linguistics, Indiana University Spring 2013 The web provides new opportunities for gathering data Viable source of disposable corpora, built ad hoc for specific purposes
More informationCharacterizing and Processing Robot-Directed Speech
Characterizing and Processing Robot-Directed Speech Paulina Varchavskaia, Paul Fitzpatrick, Cynthia Breazeal AI Lab, MIT, Cambridge, USA [paulina,paulfitz,cynthia]@ai.mit.edu Abstract. Speech directed
More informationUsing Synonyms for Author Recognition
Using Synonyms for Author Recognition Abstract. An approach for identifying authors using synonym sets is presented. Drawing on modern psycholinguistic research, we justify the basis of our theory. Having
More informationPhonetic- 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 informationUnsupervised Acoustic Model Training for Simultaneous Lecture Translation in Incremental and Batch Mode
Unsupervised Acoustic Model Training for Simultaneous Lecture Translation in Incremental and Batch Mode Diploma Thesis of Michael Heck At the Department of Informatics Karlsruhe Institute of Technology
More informationSpeaker 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 informationWhat do Medical Students Need to Learn in Their English Classes?
ISSN - Journal of Language Teaching and Research, Vol., No., pp. 1-, May ACADEMY PUBLISHER Manufactured in Finland. doi:.0/jltr...1- What do Medical Students Need to Learn in Their English Classes? Giti
More informationTaking into Account the Oral-Written Dichotomy of the Chinese language :
Taking into Account the Oral-Written Dichotomy of the Chinese language : The division and connections between lexical items for Oral and for Written activities Bernard ALLANIC 安雄舒长瑛 SHU Changying 1 I.
More informationCalibration 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 informationMachine Learning and Development Policy
Machine Learning and Development Policy Sendhil Mullainathan (joint papers with Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, Ziad Obermeyer) Magic? Hard not to be wowed But what makes
More informationIndividual Differences & Item Effects: How to test them, & how to test them well
Individual Differences & Item Effects: How to test them, & how to test them well Individual Differences & Item Effects Properties of subjects Cognitive abilities (WM task scores, inhibition) Gender Age
More informationThe Evolution of Random Phenomena
The Evolution of Random Phenomena A Look at Markov Chains Glen Wang glenw@uchicago.edu Splash! Chicago: Winter Cascade 2012 Lecture 1: What is Randomness? What is randomness? Can you think of some examples
More informationOPTIMIZATINON 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 informationCS/SE 3341 Spring 2012
CS/SE 3341 Spring 2012 Probability and Statistics in Computer Science & Software Engineering (Section 001) Instructor: Dr. Pankaj Choudhary Meetings: TuTh 11 30-12 45 p.m. in ECSS 2.412 Office: FO 2.408-B
More informationSpeech Translation for Triage of Emergency Phonecalls in Minority Languages
Speech Translation for Triage of Emergency Phonecalls in Minority Languages Udhyakumar Nallasamy, Alan W Black, Tanja Schultz, Robert Frederking Language Technologies Institute Carnegie Mellon University
More informationThe Effect of Extensive Reading on Developing the Grammatical. Accuracy of the EFL Freshmen at Al Al-Bayt University
The Effect of Extensive Reading on Developing the Grammatical Accuracy of the EFL Freshmen at Al Al-Bayt University Kifah Rakan Alqadi Al Al-Bayt University Faculty of Arts Department of English Language
More informationAtypical Prosodic Structure as an Indicator of Reading Level and Text Difficulty
Atypical Prosodic Structure as an Indicator of Reading Level and Text Difficulty Julie Medero and Mari Ostendorf Electrical Engineering Department University of Washington Seattle, WA 98195 USA {jmedero,ostendor}@uw.edu
More information