LEADER 00935nam 2200253la 450 001 9910482264903321 005 20221108030903.0 035 $a(UK-CbPIL)2090303706 035 $a(CKB)5500000000090523 035 $a(EXLCZ)995500000000090523 100 $a20210618d1583 uy | 101 0 $afre 135 $aurcn||||a|bb| 200 10$aLettre d'vn gentil-homme de Haynavt, a vn sien amy de Gand, Touchant l'estat present des affaires du Pays bas$b[electronic resource] 210 $aNetherlands $c[s.n.]$d1583 215 $aOnline resource (16 p, 4°) 300 $aReproduction of original in Koninklijke Bibliotheek, Nationale bibliotheek van Nederland. 700 $aAnon$0815482 801 0$bUk-CbPIL 801 1$bUk-CbPIL 906 $aBOOK 912 $a9910482264903321 996 $aLettre d'vn gentil-homme de Haynavt, a vn sien amy de Gand, Touchant l'estat present des affaires du Pays bas$92213052 997 $aUNINA LEADER 05164nam 2200493 450 001 996464381903316 005 20231110224528.0 010 $a3-030-89579-3 035 $a(CKB)4950000000281553 035 $a(MiAaPQ)EBC6784264 035 $a(Au-PeEL)EBL6784264 035 $a(OCoLC)1277149123 035 $z(PPN)258846941 035 $a(PPN)258296461 035 $a(EXLCZ)994950000000281553 100 $a20220712d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aStatistical language and speech processing $e9th international conference, SLSP 2021, Cardiff, UK, November 23-25, 2021 : proceedings /$fedited by Luis Espinosa-Anke, Carlos Marti?n-Vide, Irena Spasic? 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$d©2021 215 $a1 online resource (119 pages) 225 1 $aLecture Notes in Computer Science ;$vv.13062 311 $a3-030-89578-5 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Organization -- Contents -- Language -- Improving German Image Captions Using Machine Translation and Transfer Learning -- 1 Related Work -- 2 Method -- 2.1 Image Captioning Datasets -- 2.2 Image Captioning Model -- 2.3 Caption Generation Methods -- 3 Evaluation -- 3.1 Metrics -- 3.2 Hypothesis -- 3.3 Results -- 4 Discussion -- 5 Conclusion -- References -- Automatic News Article Generation from Legislative Proceedings: A Phenom-Based Approach -- 1 Introduction and Motivation -- 1.1 Motivation -- 1.2 Organization -- 2 Related Work -- 3 Approach and Development -- 3.1 Phenom System -- 3.2 Illustrative Phenom Examples -- 3.3 Template-Based Text Generation and Planning -- 4 Research Study -- 4.1 Design and Protocol -- 4.2 Results and Discussion -- 5 Conclusion and Future Work -- References -- Comparison of Czech Transformers on Text Classification Tasks -- 1 Introduction -- 2 Related Work -- 3 Datasets -- 3.1 Pre-training Datasets -- 3.2 Text Classification Datasets -- 4 Models -- 4.1 Pre-training -- 4.2 Fine-Tuning -- 4.3 Evaluation -- 5 Results -- 6 Conclusions -- References -- Constructing Sentiment Lexicon with Game for Annotation Collection -- 1 Introduction -- 2 Related Work -- 2.1 Games with a Purpose -- 2.2 Sentiment Analysis in Slovak -- 3 Game for Sentiment Collection -- 4 Experiments and Evaluation -- 4.1 Sentiment Lexicon -- 5 Conclusion -- References -- Robustness of Named Entity Recognition: Case of Latvian -- 1 Introduction -- 2 Data -- 3 Models -- 3.1 Impact of Error Types on NER -- 3.2 Data Augmentation and Robustness -- 3.3 Finding a Robust Model -- 4 Conclusions -- References -- Speech -- Use of Speaker Metadata for Improving Automatic Pronunciation Assessment -- 1 Introduction -- 2 The ASR Approach for Automatic Pronunciation Assessment -- 3 A Segment Based Approach for Mispronunciation Detection. 327 $a4 Attention Based Model -- 5 The Data -- 6 Encoding of Speaker Factors -- 7 Experiments -- 8 Results and Discussion -- 9 Conclusions -- References -- Augmenting ASR for User-Generated Videos with Semi-supervised Training and Acoustic Model Adaptation for Spoken Content Retrieval -- 1 Introduction -- 2 Semi-supervised Acoustic and Language Modelling -- 3 Adaptation of Acoustic Model Using Content Genre -- 4 ASR Experiments -- 4.1 Creation of Manual Transcripts for Blip10000 -- 4.2 Experimental Setup -- 4.3 Experimental Results -- 5 SCR Experiments -- 5.1 Creation of Known-Item Queries for Blip10000 -- 5.2 Experimental Setup -- 5.3 Experimental Results and Analysis -- 6 Conclusions and Further Work -- References -- Various DNN-HMM Architectures Used in Acoustic Modeling with Single-Speaker and Single-Channel -- 1 Introduction -- 2 Training and Test Data Set -- 3 Experimental Setup -- 3.1 Acoustic Feature Extraction -- 3.2 Acoustic Modeling -- 3.3 Language Modeling -- 3.4 Decoding -- 4 Experiments -- 4.1 HMM-topology and Context-Dependent Modeling -- 4.2 DNN Context -- 4.3 Implementation Issues -- 4.4 Amount of Training Data -- 5 Conclusion -- References -- Invariant Representation Learning for Robust Far-Field Speaker Recognition -- 1 Introduction -- 2 Base Model Architecture -- 2.1 Encoder Network: ResNet34 -- 2.2 Classification Layer: Additive Margin Softmax (AM-Softmax) -- 2.3 Back-End -- 3 Invariant Representation Learning (IRL) -- 3.1 Text-Dependent IRL (TD-IRL) -- 3.2 Text Independent IRL (TI-IRL) -- 3.3 Deep Features IRL (DF-IRL) -- 4 Experimental Setup -- 4.1 VOiCES Dataset -- 4.2 Base Model Training -- 4.3 Data Split for Evaluation -- 5 Results -- 6 Conclusions -- References -- Author Index. 410 0$aLecture Notes in Computer Science 606 $aSpeech processing systems 615 0$aSpeech processing systems. 676 $a006.35 702 $aEspinosa-Anke$b Luis 702 $aMarti?n Vide$b Carlos 702 $aSpasic?$b Irena 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996464381903316 996 $aStatistical language and speech processing$91972604 997 $aUNISA