Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Paralel Model Kombinasyonu ve Yerel Öznitelikler Kullanarak Gürbüz Konuşmacı Onaylama(Institute of Electrical and Electronics Engineers Inc., 2004) Tüfekçi, Zekeriya; 01. Izmir Institute of TechnologyInterfering 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) [1] as speech features local in frequency domain. In this paper, we 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.Article Citation - WoS: 6Citation - Scopus: 7Convolutional Bias Removal Based on Normalizing the Filterbank Spectral Magnitude(Institute of Electrical and Electronics Engineers Inc., 2007) Tüfekçi, Zekeriya; 01. Izmir Institute of TechnologyIn this letter, a novel convolutional bias removal technique is proposed. The proposed method is based on scaling the filterbank magnitude by the average of filterbank magnitude over time. The relation between the cepstral mean normalization (CMN) and proposed algorithm is derived. The experimental results show that the proposed algorithm is more robust than the CMN for both convolutional bias and additive noise. For example, the proposed method reduced the equal error rate by 5.66% and 10.16% on average for the convolutional bias and 12-dB additive noise, respectively.