Deep Neural Network Acoustic Model Baseline for Character-Level Transcription of Naturally Spoken Czech Language

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F20%3A43921328" target="_blank" >RIV/60461373:22340/20:43921328 - isvavai.cz</a>

Result on the web

<a href="https://www-scopus-com.ezproxy.vscht.cz/record/display.uri?eid=2-s2.0-85098194179&origin=resultslist&sort=plf-f&src=s&st1=&st2=&sid=456c730de23256c12af0d4a383bd2de8&sot=b&sdt=b&sl=128&s=TITLE-ABS-KEY+%28Deep+Neural+Network+Acoustic+Model+Baseline+for+Character-Level+Transcription+of+Naturally+Spoken+Czech+Language%29&relpos=0&citeCnt=0&searchTerm=" target="_blank" >https://www-scopus-com.ezproxy.vscht.cz/record/display.uri?eid=2-s2.0-85098194179&origin=resultslist&sort=plf-f&src=s&st1=&st2=&sid=456c730de23256c12af0d4a383bd2de8&sot=b&sdt=b&sl=128&s=TITLE-ABS-KEY+%28Deep+Neural+Network+Acoustic+Model+Baseline+for+Character-Level+Transcription+of+Naturally+Spoken+Czech+Language%29&relpos=0&citeCnt=0&searchTerm=</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-63322-6_14" target="_blank" >10.1007/978-3-030-63322-6_14</a>

Result language

angličtina

Original language name

Deep Neural Network Acoustic Model Baseline for Character-Level Transcription of Naturally Spoken Czech Language

Original language description

Heavy portion of previous work in automated speech recognition (ASR) conducts their research and provides results exclusively for English spoken language, for which there are many ready-to-use datasets publicly available. ASR research for other less widely utilized languages suffers from the lack of high-quality speech datasets and insufficient experimental results to compare with new research. In this article, we aim to remedy this problem for the Czech language by proposing Deep Neural Network-based Acoustic Model (AM) architecture as well as providing experimental results for character-level transcription of naturally spoken utterances. The AM architecture was developed by utilizing working solutions from modern English language ASR research and further tailored for better performance on Czech language datasets by conducting a comparative experimental study for several versions of the AM. The models were trained on up to 331 h of naturally spoken Czech language utterances from the PDTSC 1.0 and ORAL2013 datasets and validated on a 5.5-hour excerpt from the PDTSC 1.0 dataset. The results show that our final AM architecture can reach an average of 26.6 % transcript character error rate (CER) on the validation set when trained with all of the available training data. We believe that the final AM architecture presented in this paper and the experimental results can serve as a baseline for further Czech language ASR research.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2020

Confidentiality

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

Article name in the collection

4th Computational Methods in Systems and Software, CoMeSySo 2020

ISBN

978-3-030-63321-9

ISSN

2194-5357

e-ISSN

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Number of pages

16

Pages from-to

170-185

Publisher name

Springer

Place of publication

Heidelberg

Event location

Vsetín

Event date

Oct 14, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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