Using X-vectors for Speech Activity Detection in Broadcast Streams

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Using X-vectors for Speech Activity Detection in Broadcast Streams

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Using X-vectors for Speech Activity Detection in Broadcast Streams

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/TH03010018" target="_blank" >TH03010018: DeepSpot - Multilingvální technologie pro detekci a včasné upozornění</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

ISBN

978-171383690-2

ISSN

2308-457X

e-ISSN

—

Počet stran výsledku

5

Strana od-do

4161 - 4165

Název nakladatele

ISCA

Místo vydání

—

Místo konání akce

Brno, ČR

Datum konání akce

1. 1. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000841879501118