Hierarchical Discriminative Model for Spoken Language Understanding Based on Convolutional Neural Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F15%3A43926617" target="_blank" >RIV/49777513:23520/15:43926617 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.isca-speech.org/archive/interspeech_2015/papers/i15_1864.pdf" target="_blank" >http://www.isca-speech.org/archive/interspeech_2015/papers/i15_1864.pdf</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Hierarchical Discriminative Model for Spoken Language Understanding Based on Convolutional Neural Network

Popis výsledku v původním jazyce

This paper presents a novel method for processing automatic speech recognition (ASR) lattices (and generally weighted finite state acceptors) in feed-forward artificial neural networks. It is based on the existing work focused on the text classification using convolutional neural networks (CNNs). The presented method generalizes the convolutional layer of the neural network so that it is able to process both the posterior probabilities and the lexical information contained in an ASR lattice. The convolutional layer was used in a CNN-based implementation of a hierarchical discriminative model (HDM). The method was evaluated using two semantically annotated corpora and the CNN-based HDM improves performance of a spoken language understanding module in comparison with an original HDM based on Support Vector Machines (SVM)

Název v anglickém jazyce

Hierarchical Discriminative Model for Spoken Language Understanding Based on Convolutional Neural Network

Popis výsledku anglicky

This paper presents a novel method for processing automatic speech recognition (ASR) lattices (and generally weighted finite state acceptors) in feed-forward artificial neural networks. It is based on the existing work focused on the text classification using convolutional neural networks (CNNs). The presented method generalizes the convolutional layer of the neural network so that it is able to process both the posterior probabilities and the lexical information contained in an ASR lattice. The convolutional layer was used in a CNN-based implementation of a hierarchical discriminative model (HDM). The method was evaluated using two semantically annotated corpora and the CNN-based HDM improves performance of a spoken language understanding module in comparison with an original HDM based on Support Vector Machines (SVM)

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 16th Annual Conference of the International Speech Communication Association (Interspeech 2015)

ISBN

978-1-5108-1790-6

ISSN

2308-457X

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1864-1868

Název nakladatele

Curran Associates, Inc.

Místo vydání

Red Hook, NY

Místo konání akce

Dresden, Germany

Datum konání akce

6. 9. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000380581600387