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Ask2Mask: Guided Data Selection for Masked Speech Modeling

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU147442" target="_blank" >RIV/00216305:26230/22:PU147442 - isvavai.cz</a>

  • Result on the web

    <a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9806175" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9806175</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/JSTSP.2022.3186162" target="_blank" >10.1109/JSTSP.2022.3186162</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Ask2Mask: Guided Data Selection for Masked Speech Modeling

  • Original language description

    Masked speech modeling (MSM) methods such as wav2vec2 or w2v-BERT learn representations over speech frames which are randomlymaskedwithin an utterance. While thesemethods 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 in two different ways: 1) A fine-grained data selection is performed by masking over the highly confident input frames as chosen by the scorer. This allows themodel to learnmeaningful representations. 2) ATM is further extended to focus at utterance-level by weighting the final MSM loss with the utterance-level confidence score. We conduct fine-tuning experiments on two well-benchmarked corpora: LibriSpeech (matching the pre-training data) and Commonvoice, TED-LIUM, 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 (up to 11.6% relative over published results and upto 4.46% relative over our internal baseline) while still yielding modest improvements under matched conditions.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • 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

    R - Projekt Ramcoveho programu EK

Others

  • Publication year

    2022

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    IEEE J-STSP

  • ISSN

    1932-4553

  • e-ISSN

    1941-0484

  • Volume of the periodical

    16

  • Issue of the periodical within the volume

    6

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    10

  • Pages from-to

    1357-1366

  • UT code for WoS article

    000870301500019

  • EID of the result in the Scopus database

    2-s2.0-85133786585