Investigation on LP-residual representations for speaker identification

Chétouani, Mohamed; Faundez-Zanuy, Marcos; Gas, Bruno; Zarader, Jean-Luc

doi:10.1016/j.patcog.2008.08.008

Cited by 34 publications

(16 citation statements)

References 30 publications

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“…Higher order statistics also got attention of the researchers for retrieving information from the LP residual signals (Nemer et al, 2001) in voice activity detection. Recently, higher order cumulant of LP residual signal has been investigated (Chetouani et al, 2009) for improving the performance of SI system. Higher order statistical moments of a signal parameterises the shape of a function (Lo and Don, 1989).…”

Section: Statistical Moments Of Residual Signalmentioning

confidence: 99%

On the use of perceptual Line Spectral pairs Frequencies and higher-order residual moments for Speaker Identification

Sahidullah¹,

Chakroborty²,

Saha³

2010

IJBM

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Conventional Speaker Identification (SI) systems utilise spectral features like Mel-Frequency Cepstral Coefficients (MFCC) or PerceptualLinear Prediction (PLP) as a frontend module. Line Spectral pairs Frequencies (LSF) are popular alternative representation of Linear Prediction Coefficients (LPC). In this paper, an investigation is carried out to extract LSF from perceptually modified speech. A new feature set extracted from the residual signal is also proposed. SI system based on this residual feature containing complementary information to spectral characteristics, when fused with the conventional spectral feature based system as well as the proposed perceptually modified LSF, shows improved performance.Keywords: SI; speaker identification; LSF; line spectral pairs frequencies; perceptual linear prediction; residual signal; higher order statistics.Reference to this paper should be made as follows: Sahidullah, M., Chakroborty, S. and Saha, G. (2010)

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Section: Statistical Moments Of Residual Signalmentioning

confidence: 99%

On the use of perceptual Line Spectral pairs Frequencies and higher-order residual moments for Speaker Identification

Sahidullah¹,

Chakroborty²,

Saha³

2010

IJBM

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“…Mel Frequency Cepstrum Coefficients (MFCC) of order 16 were computed. We exploit traditional speaker recognition techniques [33]. For the whole utterance U x , a posteriori probabilities are estimated resulting in the estimation of P seg (C m |U x ).…”

Section: Segmental Characterizationmentioning

confidence: 99%

Time-Scale Feature Extractions for Emotional Speech Characterization

2009

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Emotional speech characterization is an important issue for the understanding of interaction. This article discusses the time-scale analysis problem in feature extraction for emotional speech processing. We describe a computational framework for combining segmental and supra-segmental features for emotional speech detection. The statistical fusion is based on the estimation of local a posteriori class probabilities and the overall decision employs weighting factors directly related to the duration of the individual speech segments. This strategy is applied to a real-world application: detection of Italian motherese in authentic and longitudinal parent-infant interaction at home. The results suggest that short-and long-term information, respectively, represented by the short-term spectrum and the prosody parameters (fundamental frequency and energy) provide a robust and efficient time-scale analysis. A similar fusion methodology is also investigated by the use of a phonetic-specific characterization process. This strategy is motivated by the fact that there are variations across emotional states at the phoneme level. A timescale based on both vowels and consonants is proposed and it provides a relevant and discriminant feature space for acted emotion recognition. The experimental results on two different databases Berlin (German) and Aholab (Basque) show that the best performance are obtained by our phoneme-dependent approach. These findings demonstrate the relevance of taking into account phoneme dependency (vowels/consonants) for emotional speech characterization.

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“…The glottal flow can be parameterized, for instance, by fitting a physical glottal flow model to the inverse filtered signal [4]. Other approaches include wavelet transforms [5], residual phase [6], cepstral coefficients [7,8] and higher-order statistics [8] to mention a few.…”

Section: Introductionmentioning

confidence: 99%

“…[5,6,8,9]) is used as a crude estimate of the glottal flow (derivative) waveform. An alternative approach uses closed-phase covariance analysis during the portions when the vocal folds are closed [4,7,10].…”

Section: Introductionmentioning

confidence: 99%

On separating glottal source and vocal tract information in telephony speaker verification

Kinnunen

Alku

2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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The popular mel-frequency cepstral coefficients (MFCCs) capture a mixture of speaker-related, phonemic and channel information. Speaker-related information could be further broken down according to articulatory criteria. How these underlying components are exactly mixed in the features is not well understood. To this end, in this paper we aim at separating the spectra of glottal source and vocal tract using glottal inverse filtering, with an application to speaker recognition over telephone lines. Our experiments on the 10sec-10sec condition of the NIST 2006 SRE corpus suggest that the mel-frequency cepstrum of the voice source is not too useful for recognizing speakers. On the contrary, fusing the vocal tract spectrum with conventional MFCCs improves accuracy, suggesting that vocal tract information should be enhanced.

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Investigation on LP-residual representations for speaker identification

Cited by 34 publications

References 30 publications

On the use of perceptual Line Spectral pairs Frequencies and higher-order residual moments for Speaker Identification

On the use of perceptual Line Spectral pairs Frequencies and higher-order residual moments for Speaker Identification

Time-Scale Feature Extractions for Emotional Speech Characterization

On separating glottal source and vocal tract information in telephony speaker verification

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