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, ZekeriyaInterfering 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: 18Citation - Scopus: 31Applied Mel-Frequency Discrete Wavelet Coefficients and Parallel Model Compensation for Noise-Robust Speech Recognition(Elsevier, 2006) Tüfekçi, Zekeriya; Gowdy, John N.; Gürbüz, Sabri; Patterson, EricInterfering noise severely degrades the performance of a speech recognition system. The Parallel Model Compensation (PMC) technique is one of the most efficient techniques for dealing with such noise. Another approach is to use features local in the frequency domain, such as Mel-Frequency Discrete Wavelet Coefficients (MFDWCs). In this paper, we investigate the use of PMC and MFDWC features to take advantage of both noise compensation and local features (MFDWCs) to decrease the effect of noise on recognition performance. We also introduce a practical weighting technique based on the noise level of each coefficient. We evaluate the performance of several wavelet-schemes using the NOISEX-92 database for various noise types and noise levels. Finally, we compare the performance of these versus Mel-Frequency Cepstral Coefficients (MFCCs), both using PMC. Experimental results show significant performance improvements for MFDWCs versus MFCCs, particularly after compensating the HMMs using the PMC technique. The best feature vector among the six MFDWCs we tried gave 13.72 and 5.29 points performance improvement, on the average, over MFCCs for -6 and 0 dB SNR, respectively. This corresponds to 39.9% and 62.8% error reductions, respectively. Weighting the partial score of each coefficient based on the noise level further improves the performance. The average error rates for the best MFDWCs dropped from 19.57% to 16.71% and from 3.14% to 2.14% for -6 dB and 0 dB noise levels, respectively, using the weighting scheme. These improvements correspond to 14.6% and 31.8% error reductions for -6 dB and 0 dB noise levels, respectively. (c) 2006 Elsevier B.V. All rights reserved.Article Citation - WoS: 6Citation - Scopus: 7Convolutional Bias Removal Based on Normalizing the Filterbank Spectral Magnitude(Institute of Electrical and Electronics Engineers Inc., 2007) Tüfekçi, ZekeriyaIn 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.Conference Object Citation - WoS: 7Citation - Scopus: 11Noise Robust Speaker Verification Using Mel-Frequency Discrete Wavelet Coefficients and Parallel Model Compensation(Institute of Electrical and Electronics Engineers Inc., 2005) Tüfekçi, Zekeriya; Gürbüz, SabriInterfering 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, Mel-Frequency Discrete Wavelet Coefficients (MFDWCs) [1, 2] were proposed as speech features local in frequency domain. In this paper, we discuss using PMC along with MFDWCs features to take advantage of both noise compensation and local features (MFDWCs) to decrease the effect of noise on speaker verification performance. We evaluate the performance of MFDWCs using the NIST 1998 speaker recognition and NOISEX-92 databases 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 Gaussian Mixture Models (GMMs) using the PMC technique. The MFDWCs gave 5.24 and 3.23 points performance improvement on average over MFCCs for -6 dB and 0 dB SNR values, respectively. These correspond to 26.44% and 23.73% relative reductions in equal error rate (EER), respectively.