Name
Abstract | ||
|---|---|---|
This paper describes an approach to robust signal analysis using iterative parameter re-estimation of a mixture autoregressive (AR) model. The model's focus can be adjusted by initialization of the target and non-target states. The variant examined in this study uses an i.i.d. mixture AR model and is designed to tackle the spectral biasing effect caused by the voice excitation in speech signals with variable fundamental frequency. In our speaker verification experiments, this method performed competitively against standard spectrum analysis techniques in non-mismatch conditions and showed significant improvements in vocal effort mismatch conditions. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/LSP.2014.2339632 | Signal Processing Letters, IEEE |
Keywords | DocType | Volume |
iterative methods,speaker recognition,AR model,iterative parameter re-estimation,mixture autoregressive model,mixture linear prediction in speaker verification,robust signal analysis,spectral biasing effect,speech signals,variable fundamental frequency,vocal effort mismatch,voice excitation,Robust acoustic features,speaker recognition,spectrum analysis,speech feature extraction | Journal | 21 |
Issue | ISSN | Citations |
12 | 1070-9908 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jouni Pohjalainen | 1 | 1 | 0.68 |
| Cemal Hanilçi | 2 | 0 | 0.34 |
| Tomi Kinnunen | 3 | 1323 | 86.67 |
| Paavo Alku | 4 | 728 | 98.07 |