Robust Feature Extraction Using Modulation Filtering of Autoregressive Models

Ganapathy, Sriram; Mallidi, Sri Harish; Heřmanský, Hynek

doi:10.1109/taslp.2014.2329190

Cited by 42 publications

(29 citation statements)

References 20 publications

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“…In addition, the speech signal can be analyzed using higher order spectra (HOS) (also known as polyspectra), that is, spectral representations of higher order moments or cumulants of a signal (Nikias and Raghuveer 1987). Currently, most feature extraction methods are based on the autoregressive (AR) models (Ganapathy et al 2014;Mesot and Barber 2007;Han et al 2009). However, these schemes are assumed to be linear, Gaussian and minimum phase, i.e., the speech signals are normally distributed, their frequency components are uncorrelated and their statistical properties do not change over time (Oveisgharan and Shamsollahi 2004;Shekofteh and Almasganj 2013).…”

Section: Introductionmentioning

confidence: 99%

A novel method for early diagnosis of Alzheimer’s disease based on higher-order spectral estimation of spontaneous speech signals

2016

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One main challenge for medical investigators is the early diagnosis of Alzheimer's disease (AD) because it provides greater opportunities for patients to be eligible for more clinical trials. In this study, higher order spectra of human speech signals during AD were explored to analyze and compare the quadratic phase coupling of spontaneous speech signals for healthy and AD subjects using bispectrum and bicoherence. The results showed that the quadratic phase couplings of spontaneous speech signal of persons with Alzheimer's were reduced compared to healthy subject. However, the speech phase coupled harmonics shifted to the higher frequencies in Alzheimer's than healthy subjects. In addition, it was shown not only are there significant differences between Alzheimer's and control subjects in parameters estimated, but also the speech patterns appeared to have fluctuated in both types of spontaneous speech.

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Section: Introductionmentioning

confidence: 99%

A novel method for early diagnosis of Alzheimer’s disease based on higher-order spectral estimation of spontaneous speech signals

2016

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“…Smoothing of Hilbert envelopes can also be conducted using frequency domain linear prediction (FDLP) [7,8,9], a method to compute all-pole estimates for Hilbert envelopes. FDLP processing can be used on its own [8] or in conjunction with time domain linear prediction (TDLP) [10]. The technique where FDLP is followed by TDLP, known as 2-dimensional autoregressive model (2DAR), has been reported to provide better speaker verification results in reverberant conditions than when using FDLP alone [10].…”

Section: Introductionmentioning

confidence: 99%

Time-Varying Autoregressions for Speaker Verification in Reverberant Conditions

Vestman¹,

Gowda²,

Sahidullah³

et al. 2017

Interspeech 2017

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In poor room acoustics conditions, speech signals received by a microphone might become corrupted by the signals' delayed versions that are reflected from the room surfaces (e.g. wall, floor). This phenomenon, reverberation, drops the accuracy of automatic speaker verification systems by causing mismatch between the training and testing. Since reverberation causes temporal smearing to the signal, one way to tackle its effects is to study robust feature extraction, particularly based on long-time temporal feature extraction. This approach has been adopted previously in the form of 2-dimensional autoregressive (2DAR) feature extraction scheme by using frequency domain linear prediction (FDLP). In 2DAR, FDLP processing is followed by time domain linear prediction (TDLP). In the current study, we propose modifying the latter part of the 2DAR feature extraction scheme by replacing TDLP with time-varying linear prediction (TVLP) to add an extra layer of temporal processing. Our speaker verification experiments using the proposed features with the text-dependent RedDots corpus show small but consistent improvements in clean and reverberant conditions (up to 6.5%) over the 2DAR features and large improvements over the MFCC features in reverberant conditions (up to 46.5%).

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“…Instead of using the MFCCs, other types of features are proposed by the researchers to increase the robustness of the recognizers [4][5][6][7][8]. Also, since the MFCCs are widely adopted, many researchers have made effort to improve its robustness under noise by modifying, or changing, some processes in the conventional scheme [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A robust polynomial regression-based voice activity detector for speaker verification

DİŞKEN

Tüfekçi

Çevik

2017

J AUDIO SPEECH MUSIC PROC.

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Robustness against background noise is a major research area for speech-related applications such as speech recognition and speaker recognition. One of the many solutions for this problem is to detect speech-dominant regions by using a voice activity detector (VAD). In this paper, a second-order polynomial regression-based algorithm is proposed with a similar function as a VAD for text-independent speaker verification systems. The proposed method aims to separate steady noise/silence regions, steady speech regions, and speech onset/offset regions. The regression is applied independently to each filter band of a mel spectrum, which makes the algorithm fit seamlessly to the conventional extraction process of the mel-frequency cepstral coefficients (MFCCs). The kmeans algorithm is also applied to estimate average noise energy in each band for spectral subtraction. A pseudo SNR-dependent linear thresholding for the final VAD output decision is introduced based on the k-means energy centers. This thresholding considers the speech presence in each band. Conventional VADs usually neglect the deteriorative effects of the additive noise in the speech regions. Contrary to this, the proposed method decides not only for the speech presence, but also if the frame is dominated by the speech, or the noise. Performance of the proposed algorithm is compared with a continuous noise tracking method, and another VAD method in speaker verification experiments, where five different noise types at five different SNR levels were considered. The proposed algorithm showed superior verification performance both with the conventional GMM-UBM method, and the stateof-the-art i-vector method.

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Robust Feature Extraction Using Modulation Filtering of Autoregressive Models

Cited by 42 publications

References 20 publications

A novel method for early diagnosis of Alzheimer’s disease based on higher-order spectral estimation of spontaneous speech signals

A novel method for early diagnosis of Alzheimer’s disease based on higher-order spectral estimation of spontaneous speech signals

Time-Varying Autoregressions for Speaker Verification in Reverberant Conditions

A robust polynomial regression-based voice activity detector for speaker verification

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