LEADER 04965nam 22005055 450 001 996546851303316 005 20230804111743.0 010 $a3-031-40498-X 024 7 $a10.1007/978-3-031-40498-6 035 $a(CKB)28013567400041 035 $a(MiAaPQ)EBC30718652 035 $a(Au-PeEL)EBL30718652 035 $a(DE-He213)978-3-031-40498-6 035 $a(PPN)27226010X 035 $a(EXLCZ)9928013567400041 100 $a20230804d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aText, Speech, and Dialogue$b[electronic resource] $e26th International Conference, TSD 2023, Pilsen, Czech Republic, September 4?6, 2023, Proceedings /$fedited by Kamil Ek?tein, Franti?ek Pártl, Miloslav Konopík 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (383 pages) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v14102 311 $a9783031404979 327 $aText: Japanese How-to Tip Machine Reading Comprehension by Multi-task Learning based on Generative Model -- One model to rule them all: ranking Slovene summarizers -- Searching for Reasons of Transformers? Success: Memorization vs Generalization -- A Dataset and Strong Baselines for Classification of Czech News Texts -- Resolving Hungarian Anaphora with ChatGPT -- Advancing Hungarian Text Processing with HuSpaCy: Efficient and Accurate NLP Pipelines -- ParaDiom - A Parallel Corpus of Idiomatic Texts -- Measuring Sentiment Bias in Machine Translation -- Mono- and multilingual GPT-3 models for Hungarian -- The Unbearable Lightness of Morph Classification -- A German Parallel Clausal Coordinate Ellipsis Corpus that Aligns Sentences from the TüBa-D/Z Treebank with Reconstructed Canonical Forms -- Speech: Identifying Subjects Wearing a Mask from the Speech by Means of Encoded Speech Representations -- Impact of Including Pathological Speech in Pre-Training on Pathology Detection -- Morphological Tagging and Lemmatization of Spoken Corpora of Czech -- HATS: An Open data set Integrating Human Perception Applied to the Evaluation of Automatic Speech Recognition Metrics -- Online Speaker Diarization Using Optimized SE-ResNet Architecture -- CML-TTS: A Multilingual Dataset for Speech Synthesis in Low-Resource Languages (Speech (in general), Corpora and Language Resources, Spe -- Developing State-of-the-Art End-to-End ASR for Norwegian -- VITS: Quality vs. Speed Analysis -- When Whisper Meets TTS: Domain Adaptation Using Only Synthetic Speech Data -- Unsupervised Learning for Automatic Speech Recognition In Air Traffic Control Environment -- The Effect of Human-Likeliness in French Robot-Directed Speech: A Study of Speech Rate and Fluency -- An online diarization approach for streaming applications based on tree-clustering and Bayesian resegmentation -- Evaluation of Speech Representations for MOS prediction -- Unified Modeling of Multi-Domain Multi-Device ASR Systems -- Voice Cloning for Voice Disorders: Impact of Phonetic Content -- Towards End-to-end Speech-to-text Summarization -- Multilingual TTS Accent Impressions for Accented ASR -- Transfer Learning of Transformer-based Speech Recognition Models from Czech to Slovak -- Automatic Pronunciation Assessment of Non-native English based on Phonological Analysis -- Language Generalization using Active Learning in the context of Parkinson?s Disease Classification. 330 $aThis book constitutes the refereed proceedings of the 26th International Conference on Text, Speech, and Dialogue, TSD 2023, held in Pilsen, Czech Republic, during September 4?6, 2023. The 31 full papers presented together with the abstracts of 3 keynote talks were carefully reviewed and selected from 64 submissions. The conference attracts researchers not only from Central and Eastern Europe but also from other parts of the world. One of its goals has always been bringing together NLP researchers with various interests from different parts of the world and promoting their cooperation. One of the ambitions of the conference is, not only to deal with dialogue systems but also to improve dialogue among researchers in areas of NLP, i.e., among the ?text? and the ?speech? and the ?dialogue? people. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v14102 606 $aArtificial intelligence 606 $aArtificial Intelligence 615 0$aArtificial intelligence. 615 14$aArtificial Intelligence. 676 $a006.35 700 $aEkstein$b Kamil$01423644 701 $aPártl$b Frantisek$01423645 701 $aKonopík$b Miloslav$01423646 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996546851303316 996 $aText, Speech, and Dialogue$93552045 997 $aUNISA