• Refine your search:
  • 
    Keyword:
    
    • FOS Computer and information sciences (4)
    • Audio and Speech Processing eess.AS (3)
    • FOS Electrical engineering, electronic engineering, information engineering (3)
    • Sound cs.SD (3)
    • Computation and Language cs.CL (2)
    • Machine Learning cs.LG (2)
  • 
    Creator / Publisher
  • 
    Year:
    
    • 2020 (1)
    • 2018 (3)
  • 
    Medium
  • 
    Type
  • 
    BLLDB-Access

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 Hits 1 – 4 of 4 1 Learning Speaker Embedding from Text-to-Speech ... Cho, Jaejin; Zelasko, Piotr; Villalba, Jesus. - : arXiv, 2020 BASE Show details 2 Language model integration based on memory control for sequence to sequence speech recognition ... Cho, Jaejin; Watanabe, Shinji; Hori, Takaaki. - : arXiv, 2018 BASE Show details 3 Transfer learning of language-independent end-to-end ASR with language model fusion ... Inaguma, Hirofumi; Cho, Jaejin; Baskar, Murali Karthick. - : arXiv, 2018 BASE Show details 4 Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling ... Cho, Jaejin; Baskar, Murali Karthick; Li, Ruizhi; Wiesner, Matthew; Mallidi, Sri Harish; Yalta, Nelson; Karafiat, Martin; Watanabe, Shinji; Hori, Takaaki. - : arXiv, 2018 Abstract: Sequence-to-sequence (seq2seq) approach for low-resource ASR is a relatively new direction in speech research. The approach benefits by performing model training without using lexicon and alignments. However, this poses a new problem of requiring more data compared to conventional DNN-HMM systems. In this work, we attempt to use data from 10 BABEL languages to build a multi-lingual seq2seq model as a prior model, and then port them towards 4 other BABEL languages using transfer learning approach. We also explore different architectures for improving the prior multilingual seq2seq model. The paper also discusses the effect of integrating a recurrent neural network language model (RNNLM) with a seq2seq model during decoding. Experimental results show that the transfer learning approach from the multilingual model shows substantial gains over monolingual models across all 4 BABEL languages. Incorporating an RNNLM also brings significant improvements in terms of %WER, and achieves recognition performance ... Keyword: Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG; Sound cs.SD URL: https://arxiv.org/abs/1810.03459 https://dx.doi.org/10.48550/arxiv.1810.03459 BASE Hide details

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 4 Linguistik-Repository 0 IDS Publikationsserver 0 Online dissertations 0 Language Description Heritage 0