A Comparison of Acoustic Models Based on Neural Networks and Gaussian Mixtures

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F09%3A00502102" target="_blank" >RIV/49777513:23520/09:00502102 - isvavai.cz</a>

Result on the web

—

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

A Comparison of Acoustic Models Based on Neural Networks and Gaussian Mixtures

Original language description

This article tries to comapre the performance of neural network and Gaussian mixture acoustic modesl (GMMs). Since the speed-accuracy trade-off is not only dependent on the acoustic model itself, but also on the settings of decoder parameters, we have suggested a comparisonbased on egual number of active states during the decoding search. Statistical significance measures are also discussed and a new method for confidence interval computation is introduced.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

—

Project

<a href="/en/project/2C06009" target="_blank" >2C06009: Complex knowledge base tools for natural language communication with the semantic web</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2009

Confidentiality

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

Article name in the collection

Text, Speech and Dialogue

ISBN

978-3-642-04207-2

ISSN

—

e-ISSN

—

Number of pages

8

Pages from-to

—

Publisher name

Springer

Place of publication

Berlin

Event location

Plzeň

Event date

Sep 17, 2009

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000270445700039