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Using X-vectors for Speech Activity Detection in Broadcast Streams

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F21%3A00009297" target="_blank" >RIV/46747885:24220/21:00009297 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.isca-speech.org/archive/pdfs/interspeech_2021/mateju21_interspeech.pdf" target="_blank" >https://www.isca-speech.org/archive/pdfs/interspeech_2021/mateju21_interspeech.pdf</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.21437/Interspeech.2021-192" target="_blank" >10.21437/Interspeech.2021-192</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Using X-vectors for Speech Activity Detection in Broadcast Streams

  • Original language description

    A new approach to speech activity detection (SAD) is presented in this work. It allows us to reduce the complexity and computation demands, namely in services that process streaming speech, where a SAD module usually forms the first block of the data pipeline (e.g., in a platform for 24/7 broadcast transcription). Our approach utilizes x-vectors as input features so that, within the subsequent pipeline stages, these embedding instances can also directly be employed for speaker diarization and recognition. The x-vectors are extracted by feed-forward sequential memory network (FSMN), allowing for modeling long-time dependencies; they thus form an input into a computationally undemanding binary classifier, whose output is smoothed by a decoder. Evaluation is performed on the standardized QUTNOISE- TIMIT dataset as well as on broadcast data with large portions of music and background noise. The former data allows for comparison with other existing approaches. The latter shows the performance in terms of word error rate (WER) and reduction in real-time factor (RTF) of the transcription process.

  • 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/TH03010018" target="_blank" >TH03010018: DeepSpot - Multilingual technology for spotting and instant alerting</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

    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

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

  • ISBN

    978-171383690-2

  • ISSN

    2308-457X

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    4161 - 4165

  • Publisher name

    ISCA

  • Place of publication

  • Event location

    Brno, ČR

  • Event date

    Jan 1, 2021

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000841879501118