Using Deep Neural Networks for Identification of Slavic Languages from Acoustic Signal

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F18%3A00006130" target="_blank" >RIV/46747885:24220/18:00006130 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.21437/Interspeech.2018-1165" target="_blank" >http://dx.doi.org/10.21437/Interspeech.2018-1165</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21437/Interspeech.2018-1165" target="_blank" >10.21437/Interspeech.2018-1165</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Using Deep Neural Networks for Identification of Slavic Languages from Acoustic Signal

Popis výsledku v původním jazyce

This paper investigates the use of deep neural networks (DNNs) for the task of spoken language identification. Various feed-forward fully connected, convolutional and recurrent DNN architectures are adopted and compared against a baseline i-vector based system. Moreover, DNNs are also utilized for extraction of bottleneck features from the input signal. The dataset used for experimental evaluation contains utterances belonging to languages that are all related to each other and sometimes hard to distinguish even for human listeners: it is compiled from recordings of the 11 most widespread Slavic languages. We also released this Slavic dataset to the general public, because a similar collection is not publicly available through any other source. The best results were yielded by a bidirectional recurrent DNN with gated recurrent units that was fed by bottleneck features. In this case, the baseline ER was reduced from 4.2% to 1.2% and Cavg from 2.3% to 0.6%. © 2018 International Speech Communication Association. All rights reserved.

Název v anglickém jazyce

Using Deep Neural Networks for Identification of Slavic Languages from Acoustic Signal

Popis výsledku anglicky

This paper investigates the use of deep neural networks (DNNs) for the task of spoken language identification. Various feed-forward fully connected, convolutional and recurrent DNN architectures are adopted and compared against a baseline i-vector based system. Moreover, DNNs are also utilized for extraction of bottleneck features from the input signal. The dataset used for experimental evaluation contains utterances belonging to languages that are all related to each other and sometimes hard to distinguish even for human listeners: it is compiled from recordings of the 11 most widespread Slavic languages. We also released this Slavic dataset to the general public, because a similar collection is not publicly available through any other source. The best results were yielded by a bidirectional recurrent DNN with gated recurrent units that was fed by bottleneck features. In this case, the baseline ER was reduced from 4.2% to 1.2% and Cavg from 2.3% to 0.6%. © 2018 International Speech Communication Association. All rights reserved.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20206 - Computer hardware and architecture

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>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

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

—

ISSN

2308-457X

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1803-1807

Název nakladatele

ISCA

Místo vydání

Indie

Místo konání akce

Hyderabad, India

Datum konání akce

1. 1. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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