Robust Automatic Recognition of Speech with Background Music

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F17%3A00004811" target="_blank" >RIV/46747885:24220/17:00004811 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICASSP.2017.7953150" target="_blank" >http://dx.doi.org/10.1109/ICASSP.2017.7953150</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICASSP.2017.7953150" target="_blank" >10.1109/ICASSP.2017.7953150</a>

Result language
angličtina
Original language name
Robust Automatic Recognition of Speech with Background Music
Original language description
This paper addresses the task of Automatic Speech Recognition (ASR) with music in the background, where the accuracy of recognition may deteriorate significantly. To improve the robustness of ASR in this task, e.g. for broadcast news transcription or subtitles creation, we adopt two approaches: 1) multi-condition training of the acoustic models and 2) denoising autoencoders followed by acoustic model training on the preprocessed data. In the latter case, two types of autoencoders are considered: the fully connected and the convolutional network. Presented experimental results show that all the investigated techniques are able to improve the recognition of speech distorted by music significantly. For example, in the case of artificial mixtures of speech and electronic music (low Signal-to-Noise Ratio (SNR) of 0 dB), we achieved absolute improvement of accuracy by 35.8%. For real-world broadcast news and a high SNR (about 10 dB), we achieved improvement by 2.4%. The important advantage of the studied approaches is that they do not deteriorate the accuracy in scenarios with clean speech (the decrease is about 1%).
Czech name
—
Czech description
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Project
<a href="/en/project/TA04010199" target="_blank" >TA04010199: MULTILINMEDIA - Multilingual Multimedia Monitoring and Analyzing Platform</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
16 June 2017, Article number 7953150, Pages 5210-52142017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017; Hilton New Orleans RiversideNew Orleans; United States; 5 March 2017 through 9 March 2017; Category numberCFP
ISBN
978-1-5090-4117-6
ISSN
1520-6149
e-ISSN
—
Number of pages
5
Pages from-to
5210-5214
Publisher name
Institute of Electrical and Electronics Engineers Inc.
Place of publication
USA
Event location
New Orleans, USA
Event date
Jan 1, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000414286205074

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