This paper presents robust feature extraction techniques, called Mel Power Karhunen Loeve Transform Coefficients (MPKC), Mel Power Coefficients (MPC) for an isolated digit recognition. This hybrid method involves Stevens’ Power Law of Hearing and Karhunen Loeve(KL) Transform to improve noise robustness. We have evaluated the proposed methods on a Hidden Markov Model (HMM) based isolated digit r...

This paper describes a speaker identification system that uses complementary acoustic features derived from the vocal source excitation and the vocal tract system. Conventional speaker recognition systems typically adopt the cepstral coefficients, e.g., Mel-frequency cepstral coefficients (MFCC) and linear predictive cepstral coefficients (LPCC), as the representative features. The cepstral fea...

Mel-frequency cepstral coefficients (MFCCs) are efficient audio descriptors providing spectral energy measurements over short time windows of length 23 ms. These measurements, however, lose non-stationary spectral information such as transients or time-varying structures. It is shown that this information can be recovered as spectral co-occurrence coefficients. Scattering operators compute thes...

Speech Synthesis (SS) and Voice Conversion (VC) presents a genuine risk of attacks for Automatic Speaker Verification (ASV) technology. In this paper, we use our recently proposed unsupervised filterbank learning technique using Convolutional Restricted Boltzmann Machine (ConvRBM) as a frontend feature representation. ConvRBM is trained on training subset of ASV spoof 2015 challenge database. A...

This paper proposes features based on parametric representation of Fourier phase of speech for speaker verification. Direct computation of Fourier phase suffers from phase wrapping and hence we attempt parametric modelling of phase spectrum using an allpass (AP) filter. The coefficients of the AP filter are estimated by minimizing an entropy based objective function motivated from speech produc...

The significance of features derived from complex analytic domain representation of speech, for different applications, is investigated. Frequency domain linear prediction (FDLP) coefficients are derived from analytic magnitude and instantaneous frequency (IF) coefficients are derived from analytic phase of speech signals. Minimal pair ABX (MP-ABX) tasks are used to analyse different features a...

requires a robust feature extraction unit followed by a speaker modeling scheme for generalized representation of these features. Over the years, Mel-Frequency Cepstral Coefficients (MFCC) modeled on the human auditory system has been used as a standard acoustic feature set for speech related applications. On a recent contribution by authors, it has been shown that the Inverted Mel-Frequency Ce...

In this paper we propose a new fusion technique, termed Joint Cohort Normalization Fusion, where the information fusion is done prior to the likelihood ratio test in a speaker verification system. The performance of the technique is compared against two popular types of fusion: feature vector concatenation and expert opinion fusion, for fusion of Mel Frequency Cepstral Coefficients (MFCC), MFCC...

This paper addresses the performance of various statistical data fusion techniques for combining the complementary score information in speaker verification. The complementary verification scores are based on the static and delta cepstral features. Both LPCC (Linear prediction-based cepstral coefficients) and MFCC (mel-frequency cepstral coefficients) are considered in the study. The experiment...