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Reducing Domain mismatch in Self-supervised speech pre-training

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU146109" target="_blank" >RIV/00216305:26230/22:PU146109 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.isca-speech.org/archive/pdfs/interspeech_2022/baskar22_interspeech.pdf" target="_blank" >https://www.isca-speech.org/archive/pdfs/interspeech_2022/baskar22_interspeech.pdf</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.21437/Interspeech.2022-736" target="_blank" >10.21437/Interspeech.2022-736</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Reducing Domain mismatch in Self-supervised speech pre-training

  • Original language description

    Masked speech modeling (MSM) methods such as wav2vec2 or w2v-BERT learn representations over speech frames which are randomly masked within an utterance. While these methods improve performance of Automatic Speech Recognition (ASR) systems, they have one major limitation. They treat all unsupervised speech samples with equal weight, which hinders learning as not all samples have relevant information to learn meaningful representations. In this work, we address this limitation. We propose ask2mask (ATM), a novel approach to focus on specific samples during MSM pre-training. ATM employs an external ASR model or scorer to weight unsupervised input samples by performing a fine-grained data selection. ATM performs masking over the highly confident input frames as chosen by the scorer. This allows the model to learn meaningful representations. We conduct fine-tuning experiments on two well-benchmarked corpora: LibriSpeech (matching the pre-training data) and, AMI and CHiME-6 (not matching the pre-training data). The results substantiate the efficacy of ATM on significantly improving the recognition performance under mismatched conditions while still yielding modest improvements under matched conditions.

  • 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

    3028-3032

  • 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