Name
Abstract | ||
|---|---|---|
Speech signals convey various pieces of information such as the identity of its speaker, the language spoken, and the linguistic information about the text being spoken, etc. In this paper, we extract information about the cell phones from their speech records by using mel-frequency cepstrum coefficients and identify their brands and models. Closed-set identification rates of 92.56% and 96.42% have been obtained on a set of 14 different cell phones in the experiments using vector quantization and support vector machine classifiers, respectively. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TIFS.2011.2178403 | IEEE Transactions on Information Forensics and Security |
Keywords | Field | DocType |
vector quantization,brand recognition,support vector machines,feature extraction,computer model,transfer function,linguistic information,speech recognition,support vector machine,computational modeling,speaker recognition,transfer functions,mel frequency cepstral coefficient,speech | Rule-based machine translation,Mel-frequency cepstrum,Speech processing,Pattern recognition,Computer science,Support vector machine,Cepstrum,Speech recognition,Feature extraction,Speaker recognition,Vector quantization,Artificial intelligence | Journal |
Volume | Issue | ISSN |
7 | 2 | 1556-6013 |
Citations | PageRank | References |
18 | 1.13 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cemal Hanilçi | 1 | 171 | 11.23 |
| Figen Ertas | 2 | 40 | 2.92 |
| Tuncay Ertas | 3 | 18 | 1.13 |
| Ömer Eskidere | 4 | 31 | 2.48 |