Ruzelita Ngadiran

A new hybrid PSO assisted biogeography-based optimization for emotion and stress recognition from speech signal

Abstract Speech signals and glottal signals convey speakers’ emotional state along with linguistic information. To recognize speakers’ emotions and respond to it expressively is very much important for human-machine interaction. To develop a subject independent speech emotion/stress recognition system, by identifying speakers emotion from their voices, features from OpenSmile toolbox, higher order spectral features and feature selection algorithm, is proposed in this work. Feature selection plays an important role in overcoming the challenge of dimensionality in several applications. This paper proposes a new particle swarm optimization assisted Biogeography-based algorithm for feature selection. The simulations were conducted using Berlin Emotional Speech Database (BES), Surrey Audio-Visual Expressed Emotion Database (SAVEE), Speech under Simulated and Actual Stress (SUSAS) and also validated using eight benchmark datasets. These datasets are of different dimensions and classes. Totally eight different experiments were conducted and obtained the recognition rates in range of 90.31%–99.47% (BES database), 62.50%–78.44% (SAVEE database) and 85.83%–98.70% (SUSAS database). The obtained results convincingly prove the effectiveness of the proposed feature selection algorithm when compared to the previous works and other metaheuristic algorithms (BBO and PSO).

Sine–cosine algorithm for feature selection with elitism strategy and new updating mechanism

Pattern recognition is the task of choosing the pertinent and descriptive features that best describes the target concept during feature selection (FS). Choosing such descriptive features becomes a daunting task in large-volume datasets which have high dimensionality. In such cases, selecting the discriminative features with better classification accuracy is tedious. To overcome this issue, in recent times, many search heuristics have been used to select the best features from these large-volume datasets. In this work, a sine–cosine algorithm (SCA) with Elitism strategy and new best solution update mechanism is proposed to select best features/attributes to improve the classification accuracy. Improved version of SCA is named as improved sine–cosine algorithm (ISCA). Wrapper-based FS approach is used. ELM with radial basis function kernel is used as the learning algorithm. For experimentation, ISCA is tested with ten benchmark datasets. Experimental results have proved the efficiency of ISCA in achieving better classification performance along with less number of features. Both computational and time complexity has been handled by this algorithm in an expedite manner. The potency of this algorithm is proved by comparing its results with three well-known meta-heuristics such as GA, PSO and basic SCA. Finally, it can be seen that pattern classification using ISCA has been commendable in achieving better classification performance.

Hybrid BBO_PSO and higher order spectral features for emotion and stress recognition from natural speech

Display Omitted We proposed higher order spectral based Bispectral and Bicoherence features for multi-class emotion/stress recognition from speech signal.Utterances from three speech emotional databases namely BES, SAVEE and SUSAS have been used in this work.Multi-cluster feature selection, Hybrid Bio-geographical based optimization and particle swarm optimization (HBBO_PSO) are used for feature selection.Experiment results show the effectiveness and efficiency of the proposed method by yielding higher emotion/stress recognition rates. The aim of the present study is to select a set of higher order spectral features for emotion/stress recognition system. 50 Bispectral (28 features) and Bicoherence (22 features) based higher order spectral features were extracted from speech signal and its glottal waveform. These features were combined with Inter-Speech 2010 features to further improve the recognition rates. Feature subset selection (FSS) was carried out in this proposed work with the objective of maximizing emotion recognition rate for subject independent with minimum features. The FSS contains two stages: Multi-cluster feature selection was adopted in Stage 1 to reduce feature space and identify relevant feature subset from Interspeech 2010 features. In Stage 2, Biogeography based optimization (BBO), Particle swarm optimization (PSO) and proposed BBO_PSO Hybrid optimization were performed to further reduce the dimension of feature space and identify the most relevant feature subset, which has higher discrimination ability to distinguish different emotional states. The proposed method was tested in three different databases: Berlin emotional speech database (BES), Surrey audio-visual expressed emotion database (SAVEE) and Speech under simulated and actual stress (SUSAS) simulated domain. The proposed feature set was evaluated with subject independent (SI), subject dependent (SD), gender dependent male (GD-male), gender dependent female (GD-female), text independent pairwise speech (TIDPS), and text independent multi-style speech (TIDMSS) experiments by using SVM and ELM classifiers. From the results obtained, it is evident that the proposed method attained accuracies of 93.25% (SI), 100% (SD), 93.75% (GD-male), and 97.58% (GD-female) for BES; 62.38% (SI) and 76.19% (SD) for SAVEE; and 90.09% (TIDMSS), 97.04% (TIDPS Angry vs. Neutral), 98.89% (TIDPS Lombard vs. Neutral), 99.07% (TIDPS Loud vs. Neutral) for SUSAS.

An analysis of interpolation methods for super resolution images

The image such as CT scan, x - ray image, CCTV videos and hand phones camera is kind of low resolution image producers. Digital camera captured the continuous scenes and transform into discrete presentation in term of space and intensity. In sampling process it may create aliasing and information lost at frequency below the Nyquist sampling rates. Therefore the image suffered with an ill-posed problem by aliasing and loss of frequency. The problem ill-pose problem could be solved by applying Super Resolution (SR) techniques. The SR process contains of image registration, interpolation and image reconstruction. However this paper is focus on an analysis the best performance offered by interpolation techniques. An analysis procedure requires interpolation kernel inspection into frequency domain plotting to determine the best kernel response in pass and stop band. Otherwise use Peak Signal to Noise Ratio as indicator the similarity simulated with original image. In this study found the cubic spline interpolation is provided the smoother function frequency response with less ripples in stop band and good pass response. Besides that, it shows a superiorly in lead the highest PSNR for all type image tests with several of upscale. The best response and less distortion effect generated by kernel is preferable candidate to produce an efficient image application with low maintenances.

Investigation of information fusion in face and palmprint multimodal biometrics

This paper reviews several information fusion techniques and strategies in the application of multimodal biometrics system using face and palmprint images. Multimodal biometric is able to overcome several limitations in single modal biometric such as intra-class variations, less discriminative power, noise data and redundant features. By consolidating two kinds of modality a better performance can be achieved. Information fusion in multimodal biometrics can be carried out at three possible levels, i.e. feature, matching score and decision levels. Fusions at these three levels have their own attributes, thus this paper is aimed to compare their effectiveness. A specific fusion rule is necessary to combine the information at each level. Several numbers of analyses on verification and identification shows matching score fusion is able to achieve the best performance which is 98% recognition rates and 98.5% GAR at 0.1% FAR when tested using AR face and PolyU palmprint datasets.

Bispectral features and mean shift clustering for stress and emotion recognition from natural speech

Abstract A new set of features and feature enhancement techniques are proposed to recognize emotion and stress from speech signal. The speech waveforms and the glottal waveforms (derived from the recorded emotional/stress speech waveforms) were processed by using third order statistics called bispectrum and 28 (14 from speech waveforms and 14 from glottal waveforms) bispectral based features. In this work, mean shift clustering was used to enhance the discrimination ability of the extracted Bispectral Features (BSFs). Four classifiers were used to distinguish different emotional and stressed states. The performance of the proposed method is tested with three databases. Different experiments were conducted and recognition rates were achieved in the range between 93.44% and 100% for Berlin emotional speech database (BES), between 73.81% and 97.23% for Surrey audio-visual expressed emotion database (SAVEE), between 93.8% and 100% for speech under simulated and actual stress simulated domain (SUSAS) (recognition of multi-style speech under stress-neutral, loud, lombard and anger) and 100% for SUSAS actual domain (recognition of three different levels of stress – high, medium and low). The obtained results indicate that the proposed bispectral based features and mean shift clustering provide promising results to recognize emotion and stress from speech signal.

Fusion of Low Frequency Coefficients of DCT Transform Image for Face and Palmprint Multimodal Biometrics

In this paper, we propose multimodal biometric feature fusion using alternating concatenation of DCT coefficients exist in face and plamprint images. Discrete cosine transform (DCT) is used to extract low frequency features which has high discrimination feature at the top left corner of the DCT transform image. The fuse feature vector is projected to the most principal component of eigenvector to produces low dimensional fused feature vector which contains important information about the face and palmprint images. Distance classifier is then implemented as a classifier to compute the nearest distance of test feature data point with a template to evaluate the recognition process. PolyU and FERET dataset is used to validate the propose method and the result shows fusion by using alternating concatenation of face and palmprint is able to produce a better recognition rates compare to concatenation method. The best recognition rate is 95%.

Score level normalization and fusion of iris recognition

Biometric traits such as an iris texture is one of the dependable physiological biometric traits because of its uniqueness. In this paper, we explore a different approach of matching score fusion and the effect of normalization method to the fusion process. Despite a plenty of work of iris recognition methods have been proposed in recent years, many are paying attention to the feature extraction process and classification method. Less number of method focuses on the information fusion of iris images. Fusion is believed to produce a better discrimination power due to the rich information can be utilized from both of iris images. We conduct an analysis to investigate which fusion rule is able to produce the best result for iris recognition system. Experimental analysis using CASIA dataset shows sum rule fusion produces 99% recognition accuracy. The verification analysis shows the best result is GAR = 95% at the FRR = 0.1% when using min-max normalization method to preprocess the matching score before the fusion process.

An embedded delta modulator system for coding speech signals

This paper discusses the comparison between two modulation systems, pulse code modulation and delta modulation. The objective is to identify a suitable modulation for speech coding by comparing delta modulation (DM) with pulse code modulation (PCM). The reconstruction performance of the delta modulation is compared with the pulse code modulation by using MATLAB, SIMULINK and finally implemented DSP Processor for real time realization. The simulation result using SIMULINK shows that the delta modulation performs better than pulse code modulation. Hence, delta modulator is implemented for real time test on DSP board, TMS320C6416. Delta modulation is successfully implemented in real time realization and can be further improved to reduce noise for future works.