Name
Title | ||
|---|---|---|
An empirical study of confusion modeling in keyword search for low resource languages |
Abstract | ||
|---|---|---|
Keyword search, in the context of low resource languages, has emerged as a key area of research. The dominant approach in keyword search is to use Automatic Speech Recognition (ASR) as a front end to produce a representation of audio that can be indexed. The biggest drawback of this approach lies in its the inability to deal with out-of-vocabulary words and query terms that are not in the ASR system output. In this paper we present an empirical study evaluating various approaches based on using confusion models as query expansion techniques to address this problem. We present results across four languages using a range of confusion models which lead to significant improvements in keyword search performance as measured by the Maximum Term Weighted Value (MTWV) metric. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ASRU.2013.6707774 | Automatic Speech Recognition and Understanding |
Keywords | Field | DocType |
query formulation,speech recognition,vocabulary,ASR,MTWV metric,audio representation,automatic speech recognition,confusion modeling,keyword search,low resource languages,maximum term weighted value metric,out-of-vocabulary words,query terms | Front and back ends,Drawback,Query language,Computer science,Natural language processing,Artificial intelligence,Empirical research,Confusion,Query expansion,Pattern recognition,Keyword search,Speech recognition,Vocabulary | Conference |
Citations | PageRank | References |
14 | 0.72 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saraclar, M. | 1 | 14 | 0.72 |
| Abhinav Sethy | 2 | 363 | 31.16 |
| Bhuvana Ramabhadran | 3 | 1779 | 153.83 |
| Lidia Mangu | 4 | 1203 | 125.73 |