Preface This research focuses on designing the SPEech Feature Toolbox (SPEFT), a toolbox which integrates a large number of speech features into one graphic interface in the MATLAB environment. The toolbox is designed with a Graphical User Interface (GUI) interface which makes it easy to operate; it also provides batch process capability. Available features are categorized into subgroups includ...

In speech recognition systems, feature extraction can be achieved in two steps: parameter extraction and feature transformation. Feature transformation is an important step. It can concentrate the energy distributions of a speech signal onto fewer dimensions than those of parameter extraction and thus reduce the dimensionality of the system. Linear Discriminant Analysis (LDA) and Principal Comp...

With the increase in the computing capabilities of the microprocessors it has now become possible to do real time computations on speech signals and images, so automatic emotion recognition through speech signals has become an important area of research. In this paper we have discussed the methods of extracting emotional features from a regular speech signals. Index Terms —Emotional feature; fe...

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, improved Linear Predictive Coding (LPC) coefficients of the frame are employed in the feature extraction method. In the proposed speech recognition system, the static LPC coefficients + dynamic LPC coefficients of the frame were employed as a basic feature. The framework of Linear Discriminant Analysis (LDA) is used to derive an efficient and reduced-dimension speech parametric s...

This article presents a new feature extraction technique based on the temporal tracking of clusters in spectro-temporal features space. In the proposed method, auditory cortical outputs were clustered. The attributes of speech clusters were extracted as secondary features. However, the shape and position of speech clusters change during the time. The clusters temporally tracked and temporal tra...