Robust Recognition of Conversational Telephone Speech via Multi-Condition Training and Data Augmentation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F18%3A00006134" target="_blank" >RIV/46747885:24220/18:00006134 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-030-00794-2_35" target="_blank" >http://dx.doi.org/10.1007/978-3-030-00794-2_35</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-00794-2_35" target="_blank" >10.1007/978-3-030-00794-2_35</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Robust Recognition of Conversational Telephone Speech via Multi-Condition Training and Data Augmentation

Popis výsledku v původním jazyce

In this paper, we focus on automatic recognition of telephone conversational speech in scenario, when no amount of genuine telephone recordings is available for training. The training set contains only data from a significantly different domain, such as recording of broadcast news. Significant mismatch arises between training and test conditions, which leads to deteriorated performance of the resulting recognition system. We aim to diminish this mismatch using the data augmentation. Speech compression and narrow-band spectrum are significant features of the telephone speech. We apply these effects to the training dataset artificially, in order to make it more similar to the desired test conditions. Using such augmented dataset, we subsequently train an acoustic model. Our experiments show that the augmented models achieve accuracy close to the results of a model trained on genuine telephone data. Moreover, when the augmentation is applied to the real-world telephone data, further accuracy gains are achieved. © Springer Nature Switzerland AG 2018.

Název v anglickém jazyce

Robust Recognition of Conversational Telephone Speech via Multi-Condition Training and Data Augmentation

Popis výsledku anglicky

In this paper, we focus on automatic recognition of telephone conversational speech in scenario, when no amount of genuine telephone recordings is available for training. The training set contains only data from a significantly different domain, such as recording of broadcast news. Significant mismatch arises between training and test conditions, which leads to deteriorated performance of the resulting recognition system. We aim to diminish this mismatch using the data augmentation. Speech compression and narrow-band spectrum are significant features of the telephone speech. We apply these effects to the training dataset artificially, in order to make it more similar to the desired test conditions. Using such augmented dataset, we subsequently train an acoustic model. Our experiments show that the augmented models achieve accuracy close to the results of a model trained on genuine telephone data. Moreover, when the augmentation is applied to the real-world telephone data, further accuracy gains are achieved. © Springer Nature Switzerland AG 2018.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20206 - Computer hardware and architecture

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

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - 21st International Conference on Text, Speech, and Dialogue, TSD 2018

ISBN

978-303000793-5

ISSN

03029743

e-ISSN

—

Počet stran výsledku

10

Strana od-do

324-333

Název nakladatele

Springer Verlag

Místo vydání

—

Místo konání akce

Brno, Czech Republic

Datum konání akce

1. 1. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—