Publication Details

Bottle-Neck Feature Extraction Structures for Multilingual Training and Porting

GRÉZL, F.; KARAFIÁT, M. Bottle-Neck Feature Extraction Structures for Multilingual Training and Porting. In Procedia Computer Science. Procedia Computer Science. Yogyakarta: Elsevier Science, 2016. p. 144-151. ISSN: 1877-0509.
Czech title
Struktury pro extrakci bottle-neck parametrů pro multilingvální trénování a přenos mezi jazyky
Type
conference paper
Language
English
Authors
Grézl František, Ing., Ph.D. (DCGM)
Karafiát Martin, Ing., Ph.D. (DCGM)
URL
Keywords

DNN topology; Stacked Bottle-Neck; feature extraction; multilingual training; system porting

Abstract

This article describes the Bottle-Neck feature extraction structures for multilingual training and porting.

Annotation

Stacked-Bottle-Neck (SBN) feature extraction is a crucial part of modern automatic speech recognition (ASR) systems. The SBN network traditionally contains a hidden layer between the BN and output layers. Recently, we have observed that an SBN architecture without this hidden layer (i.e. direct BN-layer - output-layer connection) performs better for a single language but fails in scenarios where a network pre-trained in multilingual fashion is ported to a target language. In this paper, we describe two strategies allowing the direct-connection SBN network to indeed benefit from pre-training with a multilingual net: (1) pre-training multilingual net with the hidden layer which is discarded before porting to the target language and (2) using only the the direct- connection SBN with triphone targets both in multilingual pre-training and porting to the target language. The results are reported on IARPA-BABEL limited language pack (LLP) data.

Published
2016
Pages
144–151
Journal
Procedia Computer Science, vol. 2016, no. 81, ISSN 1877-0509
Proceedings
Procedia Computer Science
Publisher
Elsevier Science
Place
Yogyakarta
DOI
10.1016/j.procs.2016.04.042
UT WoS
000387446500020
EID Scopus
2-s2.0-84976448958
BibTeX 
@inproceedings{BUT130985,
  author="František {Grézl} and Martin {Karafiát}",
  title="Bottle-Neck Feature Extraction Structures for Multilingual Training and Porting",
  booktitle="Procedia Computer Science",
  year="2016",
  journal="Procedia Computer Science",
  volume="2016",
  number="81",
  pages="144--151",
  publisher="Elsevier Science",
  address="Yogyakarta",
  doi="10.1016/j.procs.2016.04.042",
  issn="1877-0509",
  url="http://ac.els-cdn.com/S1877050916300564/1-s2.0-S1877050916300564-main.pdf?_tid=86f349d0-241e-11e6-9aa8-00000aab0f6b&acdnat=1464362601_c282a52b5e30264cf0bbd7b0e0d440ba"
}
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