Novel speech features calculated from third-order statistics of subband-filtered speech signals are introduced and studied for robust speech recognition. These features have the potential to capture nonlinear information not represented by cepstral coefficients. Also, because the features presented in this paper are based on the third-order moments, they may be more immune to Gaussian noise tha...

Ameliorating the performances of speech recognition system is a challenging problem interesting recent researchers. In this paper, we compare two extraction methods of Mel Frequency Cepstral Coefficients used to represent stressed speech utterances in order to obtain best performances. The first method known as traditional is based on single window (taper) generally the Hamming window and the s...

In the present work we explore the influence of front-end setup on the speech recognition performance. Specifically, we study the dependence between specific parameters of the speech parameterization stage, such as speech frame size and number of Mel-frequency cepstral coefficients (MFCC), and the word error rate (WER). Our comparative evaluation is performed by employing the Sphinx-IV speech r...

In the paper recently proposed Human Factor Cepstral Coefficients (HFCC) are used to automatic recognition of pathological phoneme pronunciation in speech of impaired children and efficiency of this approach is compared to application of the standard Mel-Frequency Cepstral Coefficients (MFCC) as a feature vector. Both dynamic time warping (DTW), working on whole words or embedded phoneme patter...

In this paper, a novel filtering method in feature extraction of speech is proposed for text-independent speaker identification, called Contextual Principal Curves Filtering (CPCF). The CPCF provides a good nonlinear summary of a sequence of cepstral vectors on the time context and, the most important, keeps their intrinsic trajectory characteristics, so the CPCF algorithm do improve the cepstr...

This paper presents a new method for human recognition using the cepstral information. The proposed method consists in extracting the Linear Frequency Cepstral Coefficients (LFCC) from each heartbeat in the homomorphic domain. Thus, the Hidden Markov Model (HMM) under Hidden Markov Model Toolkit (HTK) is used for electrocardiogram (ECG) classification. To evaluate the performance of the classif...

In this paper, a new method is presented to extract robust speech features in the presence of the external noise. The proposed method based on two-dimensional Gabor filters takes in account the spectro-temporal modulation frequencies and also limits the redundancy on the feature level. The performance of the proposed feature extraction method was evaluated on isolated speech words which are ext...

In this paper, different feature extraction methods for speech recognition system such as Melfrequency cepstral coefficients (MFCC), linear predictive coefficient cepstrum (LPCC) and Bark frequency cepstral coefficients (BFCC) are implemented and the comparison is done based on average recognition accuracy. We suggest a noise robust isolated word speech recognition system which can be applied i...

This paper introduces and motivates the use of hybrid robust feature extraction technique for spoken language identification (LID) sys tem. The speech recognizers use a parametric form of a signal to get the most important distinguishable features of speech signal for recognition task. In this paper Mel-frequency cepstral coefficients (MFCC), Perceptual linear prediction coefficients (PLP) alon...

Recently recognition of emotions became very important in the field of speech and/or speaker recognition. This paper is dedicated to experimental investigation of best acoustic features obtained for purpose of gender-dependent speaker recognition from emotional speech. Four feature sets LPC (Linear Prediction Coefficients), LPCC (Linear Prediction Cepstral Coefficients), MFCC (Melfrequency Ceps...