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Dealing with Unknowns in Continual Learning for End-to-end Automatic Speech Recognition

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU147440" target="_blank" >RIV/00216305:26230/22:PU147440 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.isca-speech.org/archive/pdfs/interspeech_2022/sustek22_interspeech.pdf" target="_blank" >https://www.isca-speech.org/archive/pdfs/interspeech_2022/sustek22_interspeech.pdf</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.21437/Interspeech.2022-11139" target="_blank" >10.21437/Interspeech.2022-11139</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Dealing with Unknowns in Continual Learning for End-to-end Automatic Speech Recognition

  • Original language description

    Learning continually from data is a task executed effortlessly by humans but remains to be of significant challenge for machines. Moreover, when encountering unknown test scenarios machines fail to generalize. We propose a mathematically motivated dynamically expanding end-to-end model of independent sequence-to-sequence components trained on different data sets that avoid catastrophically forgetting knowledge acquired from previously seen data while seamlessly integrating knowledge from new data. During inference, the likelihoods of the unknown test scenario are computed using internal model activation distributions. The inference made by each independent component is weighted by the normalized likelihood values to obtain the final decision.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

  • ISBN

  • ISSN

    1990-9772

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    1046-1050

  • Publisher name

    International Speech Communication Association

  • Place of publication

    Incheon

  • Event location

    Incheon Korea

  • Event date

    Sep 18, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article