Publication detail
Region Dependent Linear Transforms in Multilingual Speech Recognition
KARAFIÁT, M. JANDA, M. ČERNOCKÝ, J. BURGET, L.
Original Title
Region Dependent Linear Transforms in Multilingual Speech Recognition
English Title
Region Dependent Linear Transforms in Multilingual Speech Recognition
Type
conference paper
Language
en
Original Abstract
In today's speech recognition systems, linear or nonlinear transformations are usually applied to post-process speech features forming input to HMM based acoustic models. In this work, we experiment with three popular transforms: HLDA,MPE-HLDA and Region Dependent Linear Transforms (RDLT), which are trained jointly with the acoustic model to extract maximum of the discriminative information from the raw features and to represent it in a form suitable for the following GMM-HMM based acoustic model. We focus on multi-lingual environments, where limited resources are available for training recognizers of many languages. Using data from GlobalPhone database, we show that, under such restrictive conditions, the feature transformations can be advantageously shared across languages and robustly trained using data from several languages.
English abstract
In today's speech recognition systems, linear or nonlinear transformations are usually applied to post-process speech features forming input to HMM based acoustic models. In this work, we experiment with three popular transforms: HLDA,MPE-HLDA and Region Dependent Linear Transforms (RDLT), which are trained jointly with the acoustic model to extract maximum of the discriminative information from the raw features and to represent it in a form suitable for the following GMM-HMM based acoustic model. We focus on multi-lingual environments, where limited resources are available for training recognizers of many languages. Using data from GlobalPhone database, we show that, under such restrictive conditions, the feature transformations can be advantageously shared across languages and robustly trained using data from several languages.
Keywords
HLDA, Region Dependent Transforms, Minimum Phone Error, fMPE, multilingual speech recognition
RIV year
2012
Released
25.03.2012
Publisher
IEEE Signal Processing Society
Location
Kyoto
ISBN
978-1-4673-0044-5
Book
Proc. International Conference on Acoustics, Speech, and Signal Processing 2012
Edition
NEUVEDEN
Edition number
NEUVEDEN
Pages from
4885
Pages to
4888
Pages count
4
URL
Documents
BibTex
@inproceedings{BUT91480,
author="Martin {Karafiát} and Miloš {Janda} and Jan {Černocký} and Lukáš {Burget}",
title="Region Dependent Linear Transforms in Multilingual Speech Recognition",
annote="In today's speech recognition systems, linear or nonlinear transformations are
usually applied to post-process speech features forming input to HMM based
acoustic models. In this work, we experiment with three popular transforms:
HLDA,MPE-HLDA and Region Dependent Linear Transforms (RDLT), which are trained
jointly with the acoustic model to extract maximum of the discriminative
information from the raw features and to represent it in a form suitable for the
following GMM-HMM based acoustic model. We focus on multi-lingual environments,
where limited resources are available for training recognizers of many languages.
Using data from GlobalPhone database, we show that, under such restrictive
conditions, the feature transformations can be advantageously shared across
languages and robustly trained using data from several languages.",
address="IEEE Signal Processing Society",
booktitle="Proc. International Conference on Acoustics, Speech, and Signal Processing 2012",
chapter="91480",
doi="10.1109/ICASSP.2012.6289014",
edition="NEUVEDEN",
howpublished="print",
institution="IEEE Signal Processing Society",
year="2012",
month="march",
pages="4885--4888",
publisher="IEEE Signal Processing Society",
type="conference paper"
}