• Refine your search:
  • 
    Keyword
  • 
    Creator / Publisher
  • 
    Year
  • 
    Medium:
    
    • Online (12)
    • Print (1)
  • 
    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 – 13 of 13 1 Improving the fusion of acoustic and text representations in RNN-T ... Zhang, Chao; Li, Bo; Lu, Zhiyun. - : arXiv, 2022 BASE Show details 2 Joint Unsupervised and Supervised Training for Multilingual ASR ... Bai, Junwen; Li, Bo; Zhang, Yu. - : arXiv, 2021 BASE Show details 3 Scaling End-to-End Models for Large-Scale Multilingual ASR ... Li, Bo; Pang, Ruoming; Sainath, Tara N.. - : arXiv, 2021 BASE Show details 4 Improving Proper Noun Recognition in End-to-End ASR By Customization of the MWER Loss Criterion ... Peyser, Cal; Sainath, Tara N.; Pundak, Golan. - : arXiv, 2020 Abstract: Proper nouns present a challenge for end-to-end (E2E) automatic speech recognition (ASR) systems in that a particular name may appear only rarely during training, and may have a pronunciation similar to that of a more common word. Unlike conventional ASR models, E2E systems lack an explicit pronounciation model that can be specifically trained with proper noun pronounciations and a language model that can be trained on a large text-only corpus. Past work has addressed this issue by incorporating additional training data or additional models. In this paper, we instead build on recent advances in minimum word error rate (MWER) training to develop two new loss criteria that specifically emphasize proper noun recognition. Unlike past work on this problem, this method requires no new data during training or external models during inference. We see improvements ranging from 2% to 7% relative on several relevant benchmarks. ... Keyword: Audio and Speech Processing eess.AS; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG; Sound cs.SD URL: https://dx.doi.org/10.48550/arxiv.2005.09756 https://arxiv.org/abs/2005.09756 BASE Hide details 5 Deliberation Model Based Two-Pass End-to-End Speech Recognition ... Hu, Ke; Sainath, Tara N.; Pang, Ruoming. - : arXiv, 2020 BASE Show details 6 Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model ... Kannan, Anjuli; Datta, Arindrima; Sainath, Tara N.. - : arXiv, 2019 BASE Show details 7 Contextual Speech Recognition with Difficult Negative Training Examples ... Alon, Uri; Pundak, Golan; Sainath, Tara N.. - : arXiv, 2018 BASE Show details 8 Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model ... Li, Bo; Sainath, Tara N.; Sim, Khe Chai. - : arXiv, 2017 BASE Show details 9 No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models ... Sainath, Tara N.; Prabhavalkar, Rohit; Kumar, Shankar. - : arXiv, 2017 BASE Show details 10 Multilingual Speech Recognition With A Single End-To-End Model ... Toshniwal, Shubham; Sainath, Tara N.; Weiss, Ron J.. - : arXiv, 2017 BASE Show details 11 Exemplar-based sparse representation features: from TIMIT to LVCSR Sainath, Tara N.; Kanevsky, Dimitri; Ramabhadran, Bhuvana... In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 19 (2011) 8, 2598-2613 BLLDB OLC Linguistik Show details 12 Applications of broad class knowledge for noise robust speech recognition Sainath, Tara N. - : Massachusetts Institute of Technology, 2009 BASE Show details 13 Acoustic landmark detection and segmentation using the McAulay-Quatieri Sinusoidal Model Sainath, Tara N. - : Massachusetts Institute of Technology, 2005 BASE Show details

Mobile view All Catalogues UB Frankfurt Linguistik 0 IDS Mannheim 0 OLC Linguistik 1 UB Frankfurt Retrokatalog 0 DNB Subject Category Language 0 Institut für Empirische Sprachwissenschaft 0 Leibniz-Centre General Linguistics (ZAS) 0 Bibliographies BLLDB 1 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 12 Linguistik-Repository 0 IDS Publikationsserver 0 Online dissertations 0 Language Description Heritage 0