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Eat: Enhanced ASR-TTS for Self-Supervised Speech Recognition

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F21%3APU142916" target="_blank" >RIV/00216305:26230/21:PU142916 - isvavai.cz</a>

  • Result on the web

    <a href="https://ieeexplore.ieee.org/document/9413375" target="_blank" >https://ieeexplore.ieee.org/document/9413375</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ICASSP39728.2021.9413375" target="_blank" >10.1109/ICASSP39728.2021.9413375</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Eat: Enhanced ASR-TTS for Self-Supervised Speech Recognition

  • Original language description

    Self-supervised ASR-TTS models suffer in out-of-domain data conditions. Here we propose an enhanced ASR-TTS (EAT) model that incorporates two main features: 1) The ASR!TTS direction is equipped with a language model reward to penalize the ASR hypotheses before forwarding it to TTS. 2) In the TTS!ASR direction, a hyper-parameter is introduced to scale the attention context from synthesized speech before sending it to ASR to handle out-ofdomain data. Training strategies and the effectiveness of the EAT model are explored under out-of-domain data conditions. The results show that EAT reduces the performance gap between supervised and self-supervised training significantly by absolute 2.6% and 2.7% on Librispeech and BABEL respectively.

  • 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

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2021

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

  • ISBN

    978-1-7281-7605-5

  • ISSN

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    6753-6757

  • Publisher name

    IEEE Signal Processing Society

  • Place of publication

    Toronto, Ontario

  • Event location

    Toronto, Canada

  • Event date

    Jun 6, 2021

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000704288407006