Voice-activity and overlapped speech detection using x-vectors

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F20%3A00008350" target="_blank" >RIV/46747885:24220/20:00008350 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-030-58323-1_40" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-58323-1_40</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-58323-1_40" target="_blank" >10.1007/978-3-030-58323-1_40</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Voice-activity and overlapped speech detection using x-vectors

Popis výsledku v původním jazyce

The x-vectors are features extracted from speech signals using pretrained deep neural networks, such that they discriminate well among different speakers. Their main application lies in speaker identification and verification. This manuscript studies, which other properties are encoded in x-vectors. The focus lies on distinguishing between speech signals/noise and utterances of a single speaker versus overlapped-speech. We attempt to show that the x-vector network is capable to extract multi-purpose features, which can be used by several simple back-end classifiers. This means a common feature extracting front-end for the tasks of voice-activity/overlapped speech detection and speaker identification. Compared to the alternative strategy, that is training of independent classifiers including feature extracting layers for each of the tasks, the common front-end saves computational time during both training and test phase.

Název v anglickém jazyce

Voice-activity and overlapped speech detection using x-vectors

Popis výsledku anglicky

The x-vectors are features extracted from speech signals using pretrained deep neural networks, such that they discriminate well among different speakers. Their main application lies in speaker identification and verification. This manuscript studies, which other properties are encoded in x-vectors. The focus lies on distinguishing between speech signals/noise and utterances of a single speaker versus overlapped-speech. We attempt to show that the x-vector network is capable to extract multi-purpose features, which can be used by several simple back-end classifiers. This means a common feature extracting front-end for the tasks of voice-activity/overlapped speech detection and speaker identification. Compared to the alternative strategy, that is training of independent classifiers including feature extracting layers for each of the tasks, the common front-end saves computational time during both training and test phase.

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)

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - 23rd International Conference on Text, Speech, and Dialogue, TSD 2020

ISBN

978-303058322-4

ISSN

03029743

e-ISSN

—

Počet stran výsledku

11

Strana od-do

366-376

Název nakladatele

Springer Nature Switzerland

Místo vydání

Switzerland

Místo konání akce

(on-line) Brno, Czech Republic

Datum konání akce

1. 1. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000611543200040