Isolated word automatic speech recognition system

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10245885" target="_blank" >RIV/61989100:27240/20:10245885 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/20:10245885

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-030-59000-0_19" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-59000-0_19</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-59000-0_19" target="_blank" >10.1007/978-3-030-59000-0_19</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Isolated word automatic speech recognition system

Popis výsledku v původním jazyce

The paper is devoted to an isolated word automatic speech recognition. The first part deals with a theoretical description of methods for speech signal processing and algorithms which can be used for automatic speech recognition such as a dynamic time warping, hidden Markov models and deep neural networks. The practical part is focused on the description of the proposal which is based on convolutional neural networks (CNN). The system was designed and implemented in Python using Keras and TensorFlow frameworks. An open audio dataset of spoken words was used for training and testing. A contribution of the paper lies in the specific proposal using CNN for automatic speech recognition and its validation. The presented results show that the proposed approach is able to achieve 94% accuracy. (C) Springer Nature Switzerland AG 2020.

Název v anglickém jazyce

Isolated word automatic speech recognition system

Popis výsledku anglicky

The paper is devoted to an isolated word automatic speech recognition. The first part deals with a theoretical description of methods for speech signal processing and algorithms which can be used for automatic speech recognition such as a dynamic time warping, hidden Markov models and deep neural networks. The practical part is focused on the description of the proposal which is based on convolutional neural networks (CNN). The system was designed and implemented in Python using Keras and TensorFlow frameworks. An open audio dataset of spoken words was used for training and testing. A contribution of the paper lies in the specific proposal using CNN for automatic speech recognition and its validation. The presented results show that the proposed approach is able to achieve 94% accuracy. (C) Springer Nature Switzerland AG 2020.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/LM2018140" target="_blank" >LM2018140: e-Infrastruktura CZ</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

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Communications in Computer and Information Science. Volume 1284

ISBN

978-3-030-58999-8

ISSN

1865-0929

e-ISSN

1865-0937

Počet stran výsledku

13

Strana od-do

252-264

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Krakov

Datum konání akce

8. 10. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—