Methods for Managing Audiovisual Data: Combining Automatic Efficiency with Human Accuracy

Project Details

Short titleMeMAD
AcronymMeMAD
StatusFinished
Effective start/end date27/12/201731/03/2021

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Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
  • Advances in subword-based HMM-DNN speech recognition across languages

    Smit, P., Virpioja, S. & Kurimo, M., Mar 2021, In: Computer Speech and Language. 66, 18 p., 101158.

    Research output: Contribution to journalArticleScientificpeer-review

    Open Access
    File
    15 Citations (Scopus)
    82 Downloads (Pure)
  • An Equal Data Setting for Attention-Based Encoder-Decoder and HMM/DNN Models: A Case Study in Finnish ASR

    Rouhe, A., Van Camp, A., Singh, M., Van Hamme, H. & Kurimo, M., 2021, Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings. Karpov, A. & Potapova, R. (eds.). p. 602-613 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12997 LNAI).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Open Access
    File
    1 Citation (Scopus)
    64 Downloads (Pure)
  • Attention-Based End-To-End Named Entity Recognition From Speech

    Porjazovski, D., Leinonen, J. & Kurimo, M., 2021, Text, Speech, and Dialogue - 24th International Conference, TSD 2021, Proceedings. Ekštein, K., Pártl, F. & Konopík, M. (eds.). p. 469 - 480 12 p. (Lecture Notes in Computer Science; vol. 12848 ).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Open Access
    File
    1 Citation (Scopus)
    79 Downloads (Pure)