Feature classification criterion for missing features mask estimation in robust speaker recognition

González, Dayana Ribas; Lara, José Ramón Calvo de

doi:10.1007/s11760-012-0299-z

Cited by 2 publications

(5 citation statements)

References 20 publications

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“…Since the model-based noise-compensation methods have a high computational burden, and they assume the prior knowledge of noise characteristics, they are not suitable for real-time usage [28], [34].…”

Section: Robust Feature Extractionmentioning

confidence: 99%

“…The score normalization techniques are utilized to increase the performance of the recognition systems against the noise, and they use imposter data-sets obtained in the same conditions of the target speaker [8], [34], [82À83]. The number of estimated parameters is kept as equal to the number of static feature coefficients in FW, STG, and FM, and they are required to operate in an offline condition [18].…”

Section: Robust Feature Extractionmentioning

confidence: 99%

“…However, it should be noted that power spectral subtraction is used in the preprocessing to suppress additive noise, which probably increased the recognition rate for both the baseline methods, and the proposed method. Two-dimensional (2D) [19À20], [23], [31], [34], [38], [47], [49], [51], [54À58] auto-regressive (AR) spectrograms are used in [43] to eliminate noise effects. In this method, two LP analysis, one in time domain, and the other in frequency domain, are applied to speech signal.…”

Section: Robust Features Against Additive Noisementioning

confidence: 99%

“…The performances of these systems are mostly dependent on the accuracy of the noise estimation. Instead of the SNR criterion, authors of [34] used several complementary features, extracted from speech spectrum, to create the mask. A supervised Bayesian classifier is trained with these features to detect reliable and noisy components.…”

Section: Robust Features Against Additive Noisementioning

confidence: 99%

See 3 more Smart Citations

A Review on Feature Extraction for Speaker Recognition under Degraded Conditions

DİŞKEN

Tüfekçi

Sarıbulut

et al. 2016

IETE Technical Review

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Speech is a signal that includes speaker's emotion, characteristic specification, phonemeinformation etc. Various methods have been proposed for speaker recognition by extracting specifications of a given utterance. Among them, short-term cepstral features are used excessively in speech, and speaker recognition areas because of their low complexity, and high performance in controlled environments. On the other hand, their performances decrease dramatically under degraded conditions such as channel mismatch, additive noise, emotional variability, etc. In this paper, a literature review on speaker-specific information extraction from speech is presented by considering the latest studies offering solutions to the aforementioned problem. The studies are categorized in three groups considering their robustness against channel mismatch, additive noise, and other degradations such as vocal effort, emotion mismatch, etc. For a more understandable representation, they are also classified into two tables by utilizing their classification methods, and used data-sets.

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Section: Robust Feature Extractionmentioning

confidence: 99%

Section: Robust Feature Extractionmentioning

confidence: 99%

Section: Robust Features Against Additive Noisementioning

confidence: 99%

Section: Robust Features Against Additive Noisementioning

confidence: 99%

See 2 more Smart Citations

A Review on Feature Extraction for Speaker Recognition under Degraded Conditions

DİŞKEN

Tüfekçi

Sarıbulut

et al. 2016

IETE Technical Review

View full text Add to dashboard Cite

show abstract

“…Similar to the VAD, vowellike regions are used in [29] and improved in [30] by including the non-vowel-like regions. Missing data approach is also investigated in several studies [31][32][33][34], where a binary time-frequency mask is constructed for the noisy spectrum to indicate reliable and unreliable features. The unreliable features are then reconstructed, or marginalized (ignored in score computation).…”

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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Feature classification criterion for missing features mask estimation in robust speaker recognition

Cited by 2 publications

References 20 publications

A Review on Feature Extraction for Speaker Recognition under Degraded Conditions

A Review on Feature Extraction for Speaker Recognition under Degraded Conditions

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

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