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Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F19%3APU134179" target="_blank" >RIV/00216305:26230/19:PU134179 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.isca-speech.org/archive/Interspeech_2019/pdfs/3167.pdf" target="_blank" >https://www.isca-speech.org/archive/Interspeech_2019/pdfs/3167.pdf</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.21437/Interspeech.2019-3167" target="_blank" >10.21437/Interspeech.2019-3167</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text

  • Original language description

    Sequence-to-sequence automatic speech recognition (ASR) models require large quantities of data to attain high performance. For this reason, there has been a recent surge in interest for unsupervised and semi-supervised training in such models. This work builds upon recent results showing notable improvements in semi-supervised training using cycle-consistency and related techniques. Such techniques derive training procedures and losses able to leverage unpaired speech and/or text data by combining ASR with Text-to-Speech (TTS) models. In particular, this work proposes a new semi-supervised loss combining an end-to-end differentiable ASR!TTS loss with TTS!ASR loss. The method is able to leverage both unpaired speech and text data to outperform recently proposed related techniques in terms of %WER. We provide extensive results analyzing the impact of data quantity and speech and text modalities and show consistent gains across WSJ and Librispeech corpora. Our code is provided in ESPnet to reproduce the experiments.

  • 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

    <a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2019

  • 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 Interspeech

  • ISBN

  • ISSN

    1990-9772

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    3790-3794

  • Publisher name

    International Speech Communication Association

  • Place of publication

    Graz

  • Event location

    INTERSPEECH 2019

  • Event date

    Sep 15, 2019

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article