Linear Prediction Residual Phase Research Articles

I-Vector-Based Speaker Verification on Limited Data Using Fusion Techniques

Abstract In many biometric applications, limited data speaker verification plays a significant role in practical-oriented systems to verify the speaker. The performance of the speaker verification system needs to be improved by applying suitable techniques to limited data condition. The limited data represent both train and test data duration in terms of few seconds. This article shows the importance of the speaker verification system under limited data condition using feature- and score-level fusion techniques. The baseline speaker verification system uses vocal tract features like mel-frequency cepstral coefficients, linear predictive cepstral coefficients and excitation source features like linear prediction residual and linear prediction residual phase as features along with i-vector modeling techniques using the NIST 2003 data set. In feature-level fusion, the vocal tract features are fused with excitation source features. As a result, on average, equal error rate (EER) is approximately equal to 4% compared to individual feature performance. Further in this work, two different types of score-level fusion are demonstrated. In the first case, fusing the scores of vocal tract features and excitation source features at score-level-maintaining modeling technique remains the same, which provides an average reduction approximately equal to 2% EER compared to feature-level fusion performance. In the second case, scores of the different modeling techniques are combined, which has resulted in EER reduction approximately equal to 4.5% compared with score-level fusion of different features.

Linear prediction residual features for automatic speaker verification anti-spoofing

Automatic speaker verification (ASV) systems are highly vulnerable against spoofing attacks. Anti-spoofing, determining whether a speech signal is natural/genuine or spoofed, is very important for improving the reliability of the ASV systems. Spoofing attacks using the speech signals generated using speech synthesis and voice conversion have recently received great interest due to the 2015 edition of Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2015). In this paper, we propose to use linear prediction (LP) residual based features for anti-spoofing. Three different features extracted from LP residual signal were compared using the ASVspoof 2015 database. Experimental results indicate that LP residual phase cepstral coefficients (LPRPC) and LP residual Hilbert envelope cepstral coefficients (LPRHEC) obtained from the analytic signal of the LP residual yield promising results for anti-spoofing. The proposed features are found to outperform standard Mel-frequency cepstral coefficients (MFCC) and Cosine Phase (CosPhase) features. LPRPC and LPRHEC features give the smallest equal error rates (EER) for eight spoofing methods out of ten spoofing attacks in comparison to MFCC and CosPhase features.

Multilingual Speaker Identification by Combining Evidence from LPR and Multitaper MFCC

AbstractIn this work, the significance of combining the evidence from multitaper mel-frequency cepstral coefficients (MFCC), linear prediction residual (LPR), and linear prediction residual phase (LPRP) features for multilingual speaker identification with the constraint of limited data condition is demonstrated. The LPR is derived from linear prediction analysis, and LPRP is obtained by dividing the LPR using its Hilbert envelope. The sine-weighted cepstrum estimators (SWCE) with six tapers are considered for multitaper MFCC feature extraction. The Gaussian mixture model–universal background model is used for modeling each speaker for different evidence. The evidence is then combined at scoring level to improve the performance. The monolingual, crosslingual, and multilingual speaker identification studies were conducted using 30 randomly selected speakers from the IITG multivariability speaker recognition database. The experimental results show that the combined evidence improves the performance by nearly 8–10% compared with individual evidence.

Limited data speaker identification

In this paper, the task of identifying the speaker using limited training and testing data is addressed. Speaker identification system is viewed as four stages namely, analysis, feature extraction, modelling and testing. The speaker identification performance depends on the techniques employed in these stages. As demonstrated by different experiments, in case of limited training and testing data condition, owing to less data, existing techniques in each stage will not provide good performance. This work demonstrates the following: multiple frame size and rate (MFSR) analysis provides improvement in the analysis stage, combination of mel frequency cepstral coefficients (MFCC), its temporal derivatives (Δ, ΔΔ), linear prediction residual (LPR) and linear prediction residual phase (LPRP) features provides improvement in the feature extraction stage and combination of learning vector quantization (LVQ) and gaussian mixture model — universal background model (GMM-UBM) provides improvement in the modelling stage. The performance is further improved by integrating the proposed techniques at the respective stages and combining the evidences from them at the testing stage. To achieve this, we propose strength voting (SV), weighted borda count (WBC) and supporting systems (SS) as combining methods at the abstract, rank and measurement levels, respectively. Finally, the proposed hierarchical combination (HC) method integrating these three methods provides significant improvement in the performance. Based on these explorations, thiswork proposes a scheme for speaker identification under limited training and testing data.

Speaker Recognition from Excitation Source Perspective

This paper gives a survey of different explorations carried out using speaker information present in the excitation source of speech for speaker recognition. The paper begins with an overview of the speaker recognition task. This is followed by a discussion on different speaker information present in speech, feature extraction methods, and types of excitation sources for speech production. Detailed descriptions on different explorations to exploit the speaker information in the excitation source are then given. These include methods based on pitch contour, jitter, shimmer, glottal flow derivative, linear prediction (LP) residual, LP residual phase, LP residual cepstrum, harmonic structure of the LP residual spectrum, and time frequency analysis of LP residual. A comparative study of all these methods is then carried out to highlight their merits and demerits. The paper is concluded by mentioning a future direction for speaker recognition from excitation source perspective.