Publication detail
Hierarchical Neural Net Architectures for Feature Extraction in ASR
GRÉZL, F. KARAFIÁT, M.
Original Title
Hierarchical Neural Net Architectures for Feature Extraction in ASR
English Title
Hierarchical Neural Net Architectures for Feature Extraction in ASR
Type
conference paper
Language
en
Original Abstract
The paper is on the incorporation of Bottle-Neck features into hierarchical architecture of classifiers. This architecture was used for feature extraction for LVCSR of meetings and the resulting features were evaluated on NIST RT'05 and RT'07 test sets.
English abstract
The paper is on the incorporation of Bottle-Neck features into hierarchical architecture of classifiers. This architecture was used for feature extraction for LVCSR of meetings and the resulting features were evaluated on NIST RT'05 and RT'07 test sets.
Keywords
Speech recognition, Feature extraction, Neural network architecture
RIV year
2010
Released
26.09.2010
Publisher
International Speech Communication Association
Location
Makuhari, Chiba
ISBN
978-1-61782-123-3
Book
Proceedings of the 11th Annual Conference of the International Speech Communication Association (INTERSPEECH 2010)
Edition
NEUVEDEN
Edition number
NEUVEDEN
Pages from
1201
Pages to
1204
Pages count
4
URL
Documents
BibTex
@inproceedings{BUT35026,
author="František {Grézl} and Martin {Karafiát}",
title="Hierarchical Neural Net Architectures for Feature Extraction in ASR",
annote="The paper is on the incorporation of Bottle-Neck features into hierarchical
architecture of classifiers. This architecture was used for feature extraction
for LVCSR of meetings and the resulting features were evaluated on NIST RT'05 and
RT'07 test sets.",
address="International Speech Communication Association",
booktitle="Proceedings of the 11th Annual Conference of the International Speech Communication Association (INTERSPEECH 2010)",
chapter="35026",
edition="NEUVEDEN",
howpublished="print",
institution="International Speech Communication Association",
journal="Proceedings of Interspeech",
number="9",
year="2010",
month="september",
pages="1201--1204",
publisher="International Speech Communication Association",
type="conference paper"
}