Detail publikace

Using Smoothed Heteroscedastic Linear Discriminant Analysis in Large Vocabulary Continuous Speech Recognition System

KARAFIÁT, M., BURGET, L., ČERNOCKÝ, J.

Originální název

Using Smoothed Heteroscedastic Linear Discriminant Analysis in Large Vocabulary Continuous Speech Recognition System

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

In the state-of-the-art speech recognition systems, Heteroscedastic Linear Discriminant Analysis (HLDA)
is becoming popular technique allowing for feature decorrelation and dimensionality reduction.
However, HLDA relies on statistics, which may not be reliably estimated when only limited amount of
training data is available. Recently, Smoothed HLDA (SHLDA) was proposed
as a robust modification of
HLDA. Previously, SHLDA was successfully used for feature combination in
small vocabulary recognition
experiments. In this work, we verify that SHLDA can be advantageously used also for Large Vocabulary
Continuous Speech Recognition.

Klíčová slova

speech recognition, LVCSR, HLDA, feature transform, dimensionality reduction

Autoři

KARAFIÁT, M., BURGET, L., ČERNOCKÝ, J.

Rok RIV

2005

Vydáno

20. 9. 2005

Místo

Edinbourgh

Strany od

1

Strany do

8

Strany počet

8

URL

https://www.fit.vutbr.cz/~karafiat/publi/2005/karafiat_mlmi2005.pdf

BibTex

@inproceedings{BUT18264,
  author="Martin {Karafiát} and Lukáš {Burget} and Jan {Černocký}",
  title="Using Smoothed Heteroscedastic Linear Discriminant Analysis in Large Vocabulary Continuous Speech Recognition System",
  booktitle="2nd Joint Workshop on Multimodal Interaction and Related Machine Learning Algorithms",
  year="2005",
  pages="8",
  address="Edinbourgh",
  url="https://www.fit.vutbr.cz/~karafiat/publi/2005/karafiat_mlmi2005.pdf"
}