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...

In this paper, a voiced-unvoiced measure is used as acoustic feature for continuous speech recognition. The voiced-unvoiced measure was combined with the standard Mel Frequency Cepstral Coefficients (MFCC) using linear discriminant analysis (LDA) to choose the most relevant features. Experiments were performed on the SieTill (German digit strings recorded over telephone line) and on the SPINE (...

Electroencephalography (EEG) is a tool that allows us to analyze brain activity with high temporal resolution. These measures, combined deep learning and digital signal processing, are widely used in neurological disorder detection emotion mental recognition. In this paper, new method for recognition presented: instantaneous frequency, spectral entropy Mel-frequency cepstral coefficients (MFCC)...

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...

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 the present work we overview some recently proposed discrete Fourier transform (DFT)and discrete wavelet packet transform (DWPT)-based speech parameterization methods and evaluate their performance on the speech recognition task. Specifically, in order to assess the practical value of these less studied speech parameterization methods, we evaluate them in a common experimental setup and comp...

In this paper identification of laryngeal disorders using cepstral parameters of human voice is investigated. Mel-frequency cepstral coefficients (MFCC), extracted from audio recordings, are further approximated, using 3 strategies: sampling, averaging, and estimation. SVM and LS-SVM categorize preprocessed data into normal, nodular, and diffuse classes. Since it is a three-class problem, vario...

New refinement schemes for voice conversion are proposed in this paper. We take mel-frequency cepstral coefficients (MFCC) as the basic feature and adopt cepstral mean subtraction to compensate the channel effects. We propose S/U/V (Silence/Unvoiced/Voiced) decision rule such that two sets of codebooks are used to capture the difference between unvoiced and voiced segments of the source speaker...

Speaker recognition is the process of recognizing the speaker based on characteristics such as pitch, tone in the speech wave.Background noise influences the overall efficiency of speaker recognition system and is still considered as one of the most challenging issue in Speaker Recognition System (SRS). Support Vector Machine (SVM) and Hidden Markov Model (HMM) are widely used techniques for sp...

Automatic musical genre classification is very useful for many musical applications. In this paper, the features of instrument distribution and instrument-based notes are proposed to represent the high-level characteristics of music. Experimental results show that the proposed features have a good performance in musical genre classification. Comparison between our proposed features with the com...