To choose the best features in data mining issues, the Relief Feature Selection Algorithm is proposed to implement the feature selection in this paper. Firstly, the data of Ionosphere from the UCI (University of California Irvine) database is used to do a simulation experiment; secondly, the proposed method is employed to do feature selection for voice signal. In this case study, the study star...

Neha Chauhan Birla Institute of Technology, Mesra, Ranchi Abstract— Speaker Recognition is the computing task of validating a user’s claimed identity using speech characteristics. Main objective of speech recognition system is to communication with a device through our voice. Mel frequency Cepstral Coefficient (MFCC) features are combined with pitch and root mean square values and tested for im...

This work examines the utility of formant frequencies and their energies in acoustic-to-articulatory inversion. For this purpose, formant frequencies and formant spectral amplitudes are automatically estimated from audio, and are treated as observations for the purpose of estimating electromagnetic articulography (EMA) coil positions. A mixture Gaussian regression model with mel-frequency cepst...

In this paper we study low-variance multi-taper spectrum estimation methods to compute the mel-frequency cepstral coefficient (MFCC) features for robust speech recognition. In speech recognition, MFCC features are usually computed from a Hamming-windowed DFT spectrum. Although windowing helps in reducing the bias of the spectrum, but variance remains high. Multitaper spectrum estimation methods...

We evaluate a new filterbank structure, yielding the harmonic structure cepstral coefficients (HSCCs), on a mismatchedsession closed-set speaker classification task. The novelty of the filterbank lies in its averaging of energy at frequencies related by harmonicity rather than by adjacency. Improvements are presented which achieve a 37%rel reduction in error rate under these conditions. The imp...

HMM applications show that they are an effective and powerful tool for modelling especially stochastic signals. For this reason, we use HMM for Timit phoneme recognition. The main goal is to study the performance of an HMM phoneme recognizer to fix on an optimal signal parameters. So, we apply different techniques of speech parameterization such as MFCC, LPCC and PLP. Then, we compare the recog...

Human recognizes speech emotions by extracting features from the speech signals received through the cochlea and later passed the information for processing. In this paper we propose the use of Mel-Frequency Cepstral Coefficient (MFCC) to extract the speech emotion information to provide both the frequency and time domain information for analysis. Since features extracted using the MFCC simulat...

Phoneme is the smallest contrastive unit in the sound system of a language. Moreover, it has a meaningful role in speech recognition. In this study, we are interesting for phonemes recognition of Timit database using HTK toolkit for HMM. The main goal is to determine the optimal parameters for the recognizer. For this reason, different speech analysis techniques were operated such as Mel Freque...

This paper presents the Automatic Genre Classification of Indian Tamil Music andWestern Music using Timbral and Fractional Fourier Transform (FrFT) based Mel Frequency Cepstral Coefficient (MFCC) features. The classifier model for the proposed system has been built using K-NN (K-Nearest Neighbours) and Support Vector Machine (SVM). In this work, the performance of various features extracted fro...

Speech processing is emerged as one of the important application area of digital signal processing. Various fields for research in speech processing are speech recognition, speaker recognition, speech synthesis, speech coding etc. The objective of automatic speaker recognition is to extract, characterize and recognize the information about speaker identity. Feature extraction is the first step ...