Neural Network Language Model with Cache

Autor:	Luděk Müller, Aleš Pražák, Zdeněk Loose, Daniel Soutner
Rok vydání:	2012
Předmět:	Czech Perplexity Artificial neural network Programming language Computer science Speech recognition Statistical model computer.software_genre language.human_language Phone language Trigram Language model Cache computer
Zdroj:	Text, Speech and Dialogue ISBN: 9783642327896 TSD
DOI:	10.1007/978-3-642-32790-2_64
Popis:	In this paper we investigate whether a combination of statistical, neural network and cache language models can outperform a basic statistical model. These models have been developed, tested and exploited for a Czech spontaneous speech data, which is very different from common written Czech and is specified by a small set of the data available and high inflection of the words. As a baseline model we used a trigram model and after its training several cache models interpolated with the baseline model have been tested and measured on a perplexity. Finally, an evaluation of the model with the lowest perplexity has been performed on speech recordings of phone calls.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::33e37675894f337b8914665952487669 https://doi.org/10.1007/978-3-642-32790-2_64 Zobrazit plný text záznamu