Audio-visual Broadcast Transcription System Using Artificial Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F21%3A00009296" target="_blank" >RIV/46747885:24220/21:00009296 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9468830" target="_blank" >https://ieeexplore.ieee.org/document/9468830</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ECMSM51310.2021.9468830" target="_blank" >10.1109/ECMSM51310.2021.9468830</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Audio-visual Broadcast Transcription System Using Artificial Neural Networks

Popis výsledku v původním jazyce

In this paper, a new system for audio and visual TV broadcast News transcription is described. In the last few years, our system for audio-only broadcast transcription has been modified with the possibility of obtaining additional visual information, especially from TV video recordings. New extension modules and algorithms mainly for visual information extraction are described in this contribution. Combined Deep Neural Networks with Hidden Markov Models (DNN-HMM) are used for audio speech signal recognition. A classification of a relevant visual signal was based on Convolutional Neural Networks (CNN). There are the additional modules for detection and identification of human faces, TV logos, and company logos in the newly developed transcription system. Another module was designed for Optical Character Recognition (OCR) of text, which occurs mainly in video recordings of TV News very often. The whole audio-visual system for broadcast transcription was tested on a relatively big database (817 hours) which has been completely transcribed. The system also includes the possibility of intelligent search in transcribed data from audio and/or visual signals.

Název v anglickém jazyce

Audio-visual Broadcast Transcription System Using Artificial Neural Networks

Popis výsledku anglicky

In this paper, a new system for audio and visual TV broadcast News transcription is described. In the last few years, our system for audio-only broadcast transcription has been modified with the possibility of obtaining additional visual information, especially from TV video recordings. New extension modules and algorithms mainly for visual information extraction are described in this contribution. Combined Deep Neural Networks with Hidden Markov Models (DNN-HMM) are used for audio speech signal recognition. A classification of a relevant visual signal was based on Convolutional Neural Networks (CNN). There are the additional modules for detection and identification of human faces, TV logos, and company logos in the newly developed transcription system. Another module was designed for Optical Character Recognition (OCR) of text, which occurs mainly in video recordings of TV News very often. The whole audio-visual system for broadcast transcription was tested on a relatively big database (817 hours) which has been completely transcribed. The system also includes the possibility of intelligent search in transcribed data from audio and/or visual signals.

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

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

2021 IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics, ECMSM 2021

ISBN

978-153861757-1

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

—

Místo konání akce

Liberec, ČR

Datum konání akce

1. 1. 2021

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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