• Refine your search:
  • 
    Keyword
  • 
    Creator / Publisher:
    
    • The Pennsylvania State University CiteSeerX Archives (8)
    • Besacier, Laurent (3)
    • Bouillon, Pierrette (3)
    • SIR.AI (3)
    • Sir.Ai (3)
    • BBN SYSTEMS AND TECHNOLOGIES CORP CAMBRIDGE MA (2)
    • Boito, Marcely Zanon (2)
    • Costa-Jussa, Marta R. (2)
    • Havard, William N. (2)
    • Mahault Garnerin (2)
    •   more
  • 
    Year:
    
    • 2021 (7)
    • 2020 (5)
    • 2019 (4)
    • 2018 (1)
    • 2016 (1)
    • 2015 (2)
    • 2014 (1)
    • 2011 (2)
    • 2008 (5)
    • 2006 (1)
    •   more
  • 
    Medium:
    
    • Online (48)
  • 
    Type
  • 
    BLLDB-Access:
    • free (48)
    • subject to license (1)

Search in the Catalogues and Directories

All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 Hits 1 – 20 of 48 1 WaveRNN checkpoint ... Sir.Ai. - : Zenodo, 2021 BASE Show details 2 Neural Vocoder Checkpoint Collections ... SIR.AI. - : Zenodo, 2021 BASE Show details 3 Neural Vocoder Checkpoint Collections ... SIR.AI. - : Zenodo, 2021 BASE Show details 4 Multilingual TTS Cloning model ... Sir.Ai. - : Zenodo, 2021 BASE Show details 5 Neural Vocoder Checkpoint Collections ... SIR.AI. - : Zenodo, 2021 BASE Show details 6 Multilingual TTS Cloning model ... Sir.Ai. - : Zenodo, 2021 BASE Show details 7 Google Assistant's Interpreter Mode: is it really an interpreter? Zouaoui, Safa. - : Université de Genève, 2021 BASE Show details 8 MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the Bible Zanon Boito, Marcely; Havard, William,; Garnerin, Mahault... In: Proceedings of The 12th Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-02611059 ; Proceedings of The 12th Language Resources and Evaluation Conference, May 2020, Marseille, France. pp.6486 - 6493 (2020) BASE Show details 9 WinoST: Evaluating Gender Bias in Speech Translation ... Costa-Jussa, Marta R.. - : Zenodo, 2020 BASE Show details 10 WinoST: Evaluating Gender Bias in Speech Translation ... Costa-Jussa, Marta R.. - : Zenodo, 2020 BASE Show details 11 A Functional Approach to Translating Greek Conditionals Entz, J.. - : SIL International, 2020 BASE Show details 12 MINTZAI: Sistemas de Aprendizaje Profundo E2E para Traducción Automática del Habla ; MINTZAI: End-to-end Deep Learning for Speech Translation Etchegoyhen, Thierry; Arzelus, Haritz; Gete, Harritxu. - : Sociedad Española para el Procesamiento del Lenguaje Natural, 2020 BASE Show details 13 MaSS - Multilingual corpus of Sentence-aligned Spoken utterances ... Boito, Marcely Zanon; Havard, William N.; Mahault Garnerin. - : Zenodo, 2019 BASE Show details 14 MaSS - Multilingual corpus of Sentence-aligned Spoken utterances ... Boito, Marcely Zanon; Havard, William N.; Mahault Garnerin. - : Zenodo, 2019 BASE Show details 15 Towards Machine Speech-to-speech Translation Nakamura, Satoshi; Sudoh, Katsuhito; Sakti, Sakriani. - 2019 BASE Show details 16 Low-resource speech translation Bansal, Sameer. - : The University of Edinburgh, 2019 Abstract: We explore the task of speech-to-text translation (ST), where speech in one language (source) is converted to text in a different one (target). Traditional ST systems go through an intermediate step where the source language speech is first converted to source language text using an automatic speech recognition (ASR) system, which is then converted to target language text using a machine translation (MT) system. However, this pipeline based approach is impractical for unwritten languages spoken by millions of people around the world, leaving them without access to free and automated translation services such as Google Translate. The lack of such translation services can have important real-world consequences. For example, in the aftermath of a disaster scenario, easily available translation services can help better co-ordinate relief efforts. How can we expand the coverage of automated ST systems to include scenarios which lack source language text? In this thesis we investigate one possible solution: we build ST systems to directly translate source language speech into target language text, thereby forgoing the dependency on source language text. To build such a system, we use only speech data paired with text translations as training data. We also specifically focus on low-resource settings, where we expect at most tens of hours of training data to be available for unwritten or endangered languages. Our work can be broadly divided into three parts. First we explore how we can leverage prior work to build ST systems. We find that neural sequence-to-sequence models are an effective and convenient method for ST, but produce poor quality translations when trained in low-resource settings. In the second part of this thesis, we explore methods to improve the translation performance of our neural ST systems which do not require labeling additional speech data in the low-resource language, a potentially tedious and expensive process. Instead we exploit labeled speech data for high-resource languages which is widely available and relatively easier to obtain. We show that pretraining a neural model with ASR data from a high-resource language, different from both the source and target ST languages, improves ST performance. In the final part of our thesis, we study whether ST systems can be used to build applications which have traditionally relied on the availability of ASR systems, such as information retrieval, clustering audio documents, or question/answering. We build proof-of-concept systems for two downstream applications: topic prediction for speech and cross-lingual keyword spotting. Our results indicate that low-resource ST systems can still outperform simple baselines for these tasks, leaving the door open for further exploratory work. This thesis provides, for the first time, an in-depth study of neural models for the task of direct ST across a range of training data settings on a realistic multi-speaker speech corpus. Our contributions include a set of open-source tools to encourage further research. Keyword: automated translation systems; automatic speech recognition system; endangered languages; neural models; sequence-to-sequence models; speech translation systems; unwritten languages URL: https://doi.org/10.7488/era/86 https://hdl.handle.net/1842/36781 BASE Hide details 17 Women in the New Testament’s Church Ministry: The Problem of Remaining Silent Santiago, Jose A In: Diligence: Journal of the Liberty University Online Religion Capstone in Research and Scholarship (2018) BASE Show details 18 A Framework for Rapid Development of Limited-Domain Speech-to-Sign Phrasal Translators Ebling, Sarah; Strasly, Irene; Bouillon, Pierrette... In: 12th International Conference on Theoretical Issues in Sign Language Research (TISLR12) (2016) (2016) BASE Show details 19 Enhancing speech to speech translation through exploitation of bilingual resources and paralinguistic information ... Tsiartas, Andreas. - : University of Southern California Digital Library (USC.DL), 2015 BASE Show details 20 Installing the ARL Phrase Book Android Application and Configuring its Dependencies Lee, Michael H In: DTIC (2015) BASE Show details Page: 1 2 3

Mobile view All Catalogues UB Frankfurt Linguistik 0 IDS Mannheim 0 OLC Linguistik 0 UB Frankfurt Retrokatalog 0 DNB Subject Category Language 0 Institut für Empirische Sprachwissenschaft 0 Leibniz-Centre General Linguistics (ZAS) 0 Bibliographies BLLDB 0 BDSL 0 IDS Bibliografie zur deutschen Grammatik 0 IDS Bibliografie zur Gesprächsforschung 0 IDS Konnektoren im Deutschen 0 IDS Präpositionen im Deutschen 0 IDS OBELEX meta 0 MPI-SHH Linguistics Collection 0 MPI for Psycholinguistics 0 Linked Open Data catalogues Annohub 0 Online resources Link directory 0 Journal directory 0 Database directory 0 Dictionary directory 0 Open access documents BASE 48 Linguistik-Repository 0 IDS Publikationsserver 0 Online dissertations 0 Language Description Heritage 0