Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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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: 67Citation - Scopus: 78Chirp Group Delay Analysis of Speech Signals(Elsevier, 2007) Bozkurt, Barış; Couvreur, Laurent; Dutoit, ThierryThis study proposes new group delay estimation techniques that can be used for analyzing resonance patterns of short-term discrete-time signals and more specifically speech signals. Phase processing or equivalently group delay processing of speech signals are known to be difficult due to large spikes in the phase/group delay functions that mask the formant structure. In this study, we first analyze in detail the z-transform zero patterns of short-term speech signals in the z-plane and discuss the sources of spikes on group delay functions, namely the zeros closely located to the unit circle. We show that windowing largely influences these patterns, therefore short-term phase processing. Through a systematic study, we then show that reliable phase/group delay estimation for speech signals can be achieved by appropriate windowing and group delay functions can reveal formant information as well as some of the characteristics of the glottal flow component in speech signals. However, such phase estimation is highly sensitive to noise and robust extraction of group delay based parameters remains difficult in real acoustic conditions even with appropriate windowing. As an alternative, we propose processing of chirp group delay functions, i.e. group delay functions computed on a circle other than the unit circle in z-plane, which can be guaranteed to be spike-free. We finally present one application in feature extraction for automatic speech recognition (ASR). We show that chirp group delay representations are potentially useful for improving ASR performance. (c) 2007 Elsevier B.V. All rights reserved.