Name
Abstract | ||
|---|---|---|
Spontaneous conversations frequently contain various non-verbal vocalizations (such as laughter). The accuracy of a speech recognizer may decrease in the case of spontaneous speech because of these non-verbal vocalization phenomena. The aim of the present research is to develop an accurate and efficient method in order to recognize laughter in spontaneous utterances. We used GMM in modeling the data and SVM for differentiating laughter from other speech events. The training and testing of the laughter detector were carried out using the BEA Hungarian spoken language database. The results show that the GMM-SVM system seems to be a particularly good method for solving this problem. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-40585-3_15 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
laughter,classification,GMM-SVM,spontaneous speech | Speech corpus,Laughter,Computer science,Support vector machine,Speech recognition,Natural language processing,Artificial intelligence,Spoken language | Conference |
Volume | ISSN | Citations |
8082 | 0302-9743 | 3 |
PageRank | References | Authors |
0.42 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tilda Neuberger | 1 | 7 | 1.30 |
| András Beke | 2 | 22 | 5.51 |