A New Design Approach for Speaker Recognition Using MFCC and VAD

Nijhawan, Geeta; Soni, M. K.

doi:10.5815/ijigsp.2013.09.07

Cited by 10 publications

(8 citation statements)

References 16 publications

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“…The Cepstrum is the inverse Fourier transform of the log spectrum. According to Figure 3, the implementation of MFCCs features can be classified into five sections [18,21] namely: 1) Preemphasis 2) Frame blocking and windowing 3) Fast Fourier Transform (FFT) 4) Mel-scaled filter bank and 5) Generate MFCCs features.…”

Section: Mel Frequency Cepstral Coefficients (Mfccs)mentioning

confidence: 99%

“…In the final section, the log for Mel-spectrum is used and transferred back to the time domain to produce the MFCCs features [22]. The reader can refer to [18,21] for further information. The bandwidth and spacing are calculated by a constant interval of Mel-frequency [21] as shown in (1): Figure 2.…”

Section: Mel Frequency Cepstral Coefficients (Mfccs)mentioning

confidence: 99%

“…The reader can refer to [18,21] for further information. The bandwidth and spacing are calculated by a constant interval of Mel-frequency [21] as shown in (1): Figure 2. The GMM-UBM block diagram [13] (1)…”

Section: Mel Frequency Cepstral Coefficients (Mfccs)mentioning

confidence: 99%

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Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database

Al-Kaltakchi

Taha

Shehab

et al. 2020

IJEECS

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<p><span lang="EN-GB">In this paper, different feature extraction and feature normalization methods are investigated for speaker recognition. With a view to give a good representation of acoustic speech signals, Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction. Then, to mitigate the effect of linear channel, Cepstral Mean-Variance Normalization (CMVN) and feature warping are utilized. The current paper investigates Text-independent speaker identification system by using 16 coefficients from both the MFCCs and PNCCs features. Eight different speakers are selected from the GRID-Audiovisual database with two females and six males. The speakers are modeled using the coupling between the Universal Background Model and Gaussian Mixture Models (GMM-UBM) in order to get a fast scoring technique and better performance. The system shows 100% in terms of speaker identification accuracy. The results illustrated that PNCCs features have better performance compared to the MFCCs features to identify females compared to male speakers. Furthermore, feature wrapping reported better performance compared to the CMVN method. </span></p>

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Section: Mel Frequency Cepstral Coefficients (Mfccs)mentioning

confidence: 99%

Section: Mel Frequency Cepstral Coefficients (Mfccs)mentioning

confidence: 99%

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Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database

Al-Kaltakchi

Taha

Shehab

et al. 2020

IJEECS

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“…In auditory cortex, receptive fields are defined as cortical circuits involving small clusters of neurons ordered topographically according to the tuning characteristics of cochlea and the clustered neurons as well as cortical circuit becoming active with specific acoustic features. But, the acoustical features of vowels consist not only specific linguistic information but also perturbed acoustical features which are generated during vowel production due to the differences of vocaltract size, shape and physical conditions of speakers [5,6]. Moreover, the environmental noises also contaminate the acoustical features of vowels.…”

Section: Introductionmentioning

confidence: 99%

Determination of Representativity for Bangla Vowel Perceptual Space

Hasan¹,

Mitra²

2018

IJITCS

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In this article, representativity between two multidimensional acoustical spaces of vowel has been formulated based on the geometric mean of correlation of average directional vector, variance-covariance matrices, and Mahalanobis distance. Generally, the multidimensional spaces formed by different combinations of acoustical features of vowel are considered as the vowel perceptual spaces. Therefore, ten bangla vowel-sounds (

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“…A pre-emphasis finite impulse response (FIR) filter realizing a first order highpass filter was employed to filter the speech samples with emphasis coefficient 0.96 [5]. In addition, framing and Hamming windowing were employed with a frame length of 16 ms with an inter-frame overlap of 8 ms [33]. Moreover, this work exploits a triangular/Mel filter bank (MFB) and the logarithmic non-linearity used in MFCC [34], as well as the Gammatone filter bank (GFB) and power law non-linearity for PNCC [31,35,36].…”

Section: Feature Extraction and Compensationmentioning

confidence: 99%

Evaluation of a speaker identification system with and without fusion using three databases in the presence of noise and handset effects

Al-Kaltakchi

Woo

Dlay

et al. 2017

EURASIP J. Adv. Signal Process.

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In this study, a speaker identification system is considered consisting of a feature extraction stage which utilizes both power normalized cepstral coefficients (PNCCs) and Mel frequency cepstral coefficients (MFCC). Normalization is applied by employing cepstral mean and variance normalization (CMVN) and feature warping (FW), together with acoustic modeling using a Gaussian mixture model-universal background model (GMM-UBM). The main contributions are comprehensive evaluations of the effect of both additive white Gaussian noise (AWGN) and non-stationary noise (NSN) (with and without a G.712 type handset) upon identification performance. In particular, three NSN types with varying signal to noise ratios (SNRs) were tested corresponding to street traffic, a bus interior, and a crowded talking environment. The performance evaluation also considered the effect of late fusion techniques based on score fusion, namely, mean, maximum, and linear weighted sum fusion. The databases employed were TIMIT, SITW, and NIST 2008; and 120 speakers were selected from each database to yield 3600 speech utterances. As recommendations from the study, mean fusion is found to yield overall best performance in terms of speaker identification accuracy (SIA) with noisy speech, whereas linear weighted sum fusion is overall best for original database recordings.

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A New Design Approach for Speaker Recognition Using MFCC and VAD

Cited by 10 publications

References 16 publications

Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database

Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database

Determination of Representativity for Bangla Vowel Perceptual Space

Evaluation of a speaker identification system with and without fusion using three databases in the presence of noise and handset effects

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