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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tufekci, Z. |
| Copyright Year | 2004 |
| Description | Author affiliation: Izmir Yuksek Teknoloji Enstitusu, Turkey (Tufekci, Z.) |
| Abstract | Interfering noise severely degrades the performance of a speaker verification system. The parallel model combination (PMC) technique is one of the most efficient techniques for dealing with such noise. Another method is to use features local in the frequency domain. Recently, we proposed Mel-frequency discrete wavelet coefficients (MFDWCs) (Gowdy, J. and Tufekci, Z., Proc. ICASSP, 2000) as speech features local in the frequency domain. We now discuss using PMC along with MFDWC features to take advantage of both noise compensation and local features (MFDWCs) to decrease the effect of noise on verification performance. We evaluate the performance of MFDWCs for various noise types and noise levels. We also compare the performance of these versus MFCCs and both using PMC for dealing with additive noise. The experimental results show significant performance improvements for MFDWCs versus MFCCs after compensating the HMMs using the PMC technique. For example, the MFDWCs gave 6.29 points performance improvement on average over MFCCs for 12 dB. This corresponds to 38.33% error reduction. |
| Sponsorship | IEEE Tubitak Istanbul Teknik Universitesi Aselsan Profilo Telra TURCom SGI Datacore Divit |
| Starting Page | 422 |
| Ending Page | 425 |
| File Size | 248903 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780383184 |
| DOI | 10.1109/SIU.2004.1338553 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-04-30 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Degradation Additive noise Hidden Markov models Speech Noise robustness Mel frequency cepstral coefficient Frequency domain analysis Noise level Testing Wavelet coefficients |
| Content Type | Text |
| Resource Type | Article |
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