Investigation of Deep Neural Networks for Robust Recognition of Nonlinearly Distorted Speech
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F14%3A%230002971" target="_blank" >RIV/46747885:24220/14:#0002971 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Investigation of Deep Neural Networks for Robust Recognition of Nonlinearly Distorted Speech
Original language description
This paper studies the use of hybrid context-dependent Deep Neural Network Hidden Markov Model (DNN-HMM) architecture for robust recognition of speech affected by realworld nonlinear distortions. We consider two types of distortions; a) signals distortedthrough overgained microphone preamplifier in the analog domain and b) recordings exhibiting unnatural spectral sparseness, caused by excessive denoising or low-bit-rate compression. We compare the performance of DNN-HMM architecture with that of the conventional system, based on context-dependent Gaussian Mixture Model (GMM)- HMMs, which applies channel/speaker adaptation and/or feature compensation in the front-end via Histogram Equalization (HEQ). We show that DNN-HMM architecture achieves a significantly lower Word Error Rate (WER) on the considered distorted datasets and that the obtained relative WER reduction is higher than 60%. We also investigate the usefulness of the feature compensation via HEQ for a DNN-HMM system and show
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Result continuities
Project
<a href="/en/project/TA01011142" target="_blank" >TA01011142: Automatic transcription and indexation of lectures</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 15th Annual Conference of the International Speech Communication Association (INTERSPEECH 2014)
ISBN
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ISSN
2308-457X
e-ISSN
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Number of pages
5
Pages from-to
363-367
Publisher name
ISCA
Place of publication
Singapure
Event location
Singapure
Event date
Jan 1, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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