In this paper, we apply a discriminative weight training to a support vector machine (SVM) based gender identification. In our approach, the gender decision rule is derived by the SVM incorporating the optimally weighted mel-frequency cepstral coefficient (MFCC) based on a minimum classification error (MCE) method which is different from the previous works in that optimal weights are differentl...

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...

This paper proposes an original statistical decision theory to accomplish a multi-speaker recognition task in cocktail party problem. This theory relies on an assumption that the varied frequencies of speakers obey Gaussian distribution and the relationship of their voiceprints can be represented by Euclidean distance vectors. This paper uses Mel-Frequency Cepstral Coefficients to extract the f...

The focus of a continuous speech recognition process is to match an input signal with a set of words or sentences according to some optimality criteria. The first step of this process is parameterization, whose major task is data reduction by converting the input signal into parameters while preserving virtually all of the speech signal information dealing with the text message. This contributi...

Existing automatic speaker verification (ASV) systems perform with high accuracy when the speech signal is collected close to the mouth of the speaker (< 1 ft). However, the performance of these systems reduces significantly when speech signals are collected at a distance from the speaker (2-6 ft). The objective of this paper is to address some issues in the processing of speech signals collect...

This paper motivates the use of Dynamic Mel-Frequency Cepstral Coefficient (DMFCC) feature and combination of DMFCC and MFCC features for robust language and text-independent speaker identification. MFCC feature, modeled on the human auditory system has been the widely used feature for speaker recognition because of its less vulnerability to noise perturbation and little session variability. Bu...

In this paper, a feature extraction algorithm for robust speech recognition is introduced. The feature extraction algorithm is motivated by the human auditory processing and the nonlinear Teager-Kaiser energy operator that estimates the true energy of the source of a resonance. The proposed features are labeled as Teager Energy Cepstrum Coefficients (TECCs). TECCs are computed by first filterin...

Techniques for music recommendation are increasingly relying on hybrid representations to retrieve new and exciting music. A key component of these representations is musical content, with texture being the most widely used feature. Current techniques for representing texture however are inspired by speech, not music, therefore music representations are not capturing the correct nature of music...