Speech Activity Detection in Online Broadcast Transcription Using Deep Neural Networks and Weighted Finite State Transducers

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F17%3A00004814" target="_blank" >RIV/46747885:24220/17:00004814 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ICASSP.2017.7953200" target="_blank" >http://dx.doi.org/10.1109/ICASSP.2017.7953200</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICASSP.2017.7953200" target="_blank" >10.1109/ICASSP.2017.7953200</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Speech Activity Detection in Online Broadcast Transcription Using Deep Neural Networks and Weighted Finite State Transducers

Popis výsledku v původním jazyce

In this paper, a new approach to online Speech Activity Detection (SAD) is proposed. This approach is designed for the use in a system that carries out 24/7 transcription of radio/TV broadcasts containing a large amount of non-speech segments, such as advertisements or music. To improve the robustness of detection, we adopt Deep Neural Networks (DNNs) trained on artificially created mixtures of speech and non-speech signals at desired levels of signal-to-noise ratio (SNR). An integral part of our approach is an online decoder based on Weighted Finite State Transducers (WFSTs); this decoder smooths the output from DNN. The employed transduction model is context-based, i.e., both speech and non-speech events are modeled using sequences of states. The presented experimental results show that our approach yields state-of-the-art results on standardized QUT-NOISE-TIMIT data set for SAD and, at the same time, it is capable of a) operating with low latency and b) reducing the computational demands and error rate of the target transcription system.

Název v anglickém jazyce

Speech Activity Detection in Online Broadcast Transcription Using Deep Neural Networks and Weighted Finite State Transducers

Popis výsledku anglicky

In this paper, a new approach to online Speech Activity Detection (SAD) is proposed. This approach is designed for the use in a system that carries out 24/7 transcription of radio/TV broadcasts containing a large amount of non-speech segments, such as advertisements or music. To improve the robustness of detection, we adopt Deep Neural Networks (DNNs) trained on artificially created mixtures of speech and non-speech signals at desired levels of signal-to-noise ratio (SNR). An integral part of our approach is an online decoder based on Weighted Finite State Transducers (WFSTs); this decoder smooths the output from DNN. The employed transduction model is context-based, i.e., both speech and non-speech events are modeled using sequences of states. The presented experimental results show that our approach yields state-of-the-art results on standardized QUT-NOISE-TIMIT data set for SAD and, at the same time, it is capable of a) operating with low latency and b) reducing the computational demands and error rate of the target transcription system.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

<a href="/cs/project/TA04010199" target="_blank" >TA04010199: MULTILINMEDIA - Multilinguální platforma pro monitoring a analýzu multimédií</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

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

2017 IEEE IICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedingsnternational Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017

ISBN

978-1-5090-4117-6

ISSN

1520-6149

e-ISSN

—

Počet stran výsledku

5

Strana od-do

5460-5464

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

USA

Místo konání akce

New Orleans, USA

Datum konání akce

1. 1. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000414286205124