A robust feature extraction with optimized DBN-SMO for facial expression recognition

Multimedia Tools and Applications - Facial expression is the most common technique is used to convey the expressions of human beings. Due to different ethnicity and age, faces differ from one...     

A real-time road pricing system: The case of a two-link parallel network

A real-time road pricing system in the case of a two-link parallel network is proposed in this paper. The system that is based on a combination of Dynamic Programming and Neural Networks makes “on-line” decisions about road toll values. In the first phase of the proposed model, the best road toll sequences during certain time period are calculated off-line for many different patterns of vehicle arrivals. These toll sequences are computed using Dynamic Programming approach. In the second phase, learning from vehicle arrival patterns and the corresponding optimal toll sequences, neural network is trained. The results obtained during on-line tests are close to the best solution obtained off-line assuming that the arrival pattern is known.     

Comparing ANN and GMM in a voice conversion framework

In this paper, we present a comparative analysis of artificial neural networks (ANNs) and Gaussian mixture models (GMMs) for design of voice conversion system using line spectral frequencies (LSFs) as feature vectors. Both the ANN and GMM based models are explored to capture nonlinear mapping functions for modifying the vocal tract characteristics of a source speaker according to a desired target speaker. The LSFs are used to represent the vocal tract transfer function of a particular speaker. Mapping of the intonation patterns (pitch contour) is carried out using a codebook based model at segmental level. The energy profile of the signal is modified using a fixed scaling factor defined between the source and target speakers at the segmental level. Two different methods for residual modification such as residual copying and residual selection methods are used to generate the target residual signal. The performance of ANN and GMM based voice conversion (VC) system are conducted using subjective and objective measures. The results indicate that the proposed ANN-based model using LSFs feature set may be used as an alternative to state-of-the-art GMM-based models used to design a voice conversion system.     

Articulatory and excitation source features for speech recognition in read,extempore and conversation modes

In our previous works, we have explored articulatory and excitation source features to improve the performance of phone recognition systems (PRSs) using read speech corpora. In this work, we have extended the use of articulatory and excitation source features for developing PRSs of extempore and conversation modes of speech, in addition to the read speech. It is well known that the overall performance of speech recognition system heavily depends on accuracy of phone recognition. Therefore, the objective of this paper is to enhance the accuracy of phone recognition systems using articulatory and excitation source features in addition to conventional spectral features. The articulatory features (AFs) are derived from the spectral features using feedforward neural networks (FFNNs). We have considered five AF groups, namely: manner, place, roundness, frontness and height. Five different AF-based tandem PRSs are developed using the combination of Mel frequency cepstral coefficients (MFCCs) and AFs derived from FFNNs. Hybrid PRSs are developed by combining the evidences from AF-based tandem PRSs using weighted combination approach. The excitation source information is derived by processing the linear prediction residual of the speech signal. The vocal tract information is captured using MFCCs. The combination of vocal tract and excitation source features is used for developing PRSs. The PRSs are developed using hidden Markov models. Bengali speech database is used for developing PRSs of read, extempore and conversation modes of speech. The results are analyzed and the performance is compared across different modes of speech. From the results, it is observed that the use of either articulatory or excitation source features along-with to MFCCs will improve the performance of PRSs in all three modes of speech. The improvement in the performance using AFs is much higher compared to the improvement obtained using excitation source features.     

Two stage emotion recognition based on speaking rate

This paper proposes two stage speech emotion recognition approach using speaking rate. The emotions considered in this study are anger, disgust, fear, happy, neutral, sadness, sarcastic and surprise. At the first stage, based on speaking rate, eight emotions are categorized into 3 broad groups namely active (fast), normal and passive (slow). In the second stage, these 3 broad groups are further classified into individual emotions using vocal tract characteristics. Gaussian mixture models (GMM) are used for developing the emotion models. Emotion classification performance at broader level, based on speaking rate is found to be around 99% for speaker and text dependent cases. Performance of overall emotion classification is observed to be improved using the proposed two stage approach. Along with spectral features, the formant features are explored in the second stage, to achieve robust emotion recognition performance in case of speaker, gender and text independent cases.     

Recognition of consonant-vowel (CV) units under background noise using combined temporal and spectral preprocessing

This paper proposes hybrid classification models and preprocessing methods for enhancing the consonant-vowel (CV) recognition in the presence of background noise. Background Noise is one of the major degradation in real-time environments which strongly effects the performance of speech recognition system. In this work, combined temporal and spectral processing (TSP) methods are explored for preprocessing to improve CV recognition performance. Proposed CV recognition method is carried out in two levels to reduce the similarity among large number of CV classes. In the first level vowel category of CV unit will be recognized, and in the second level consonant category will be recognized. At each level complementary evidences from hybrid models consisting of support vector machine (SVM) and hidden Markov models (HMM) are combined for enhancing the recognition performance. Performance of the proposed CV recognition system is evaluated on Telugu broadcast database for white and vehicle noise. The proposed preprocessing methods and hybrid classification models have improved the recognition performance compared to existed methods.     

Remote sensing and GIS for locating favourable zones of lead-zinc-copper mineralization in Rajpura-Dariba area,Rajasthan, India

This paper reports the results of a pilot study carried out in RajpuraDariba area, Rajasthan, for locating favourable zones of lead-zinc-copper (Pb-Zn-Cu) mineralization using remote sensing, Geographical Information System (GIS) and geostatistical modelling techniques. Remotely sensed data, both aerial and satellite, were used to update the existing geological map. ATLAS GIS software and multivariate geostatistical techniques were used to analyse and integrate different types of geological and geophysical datasets. The Favourability Index (FI) maps prepared during this study show the occurrence of three favourable zones for Pb-Zn-Cu mineralization. They are: (i) around and north of Rawan ka Khera; (ii) isolated spots between Ruppura and Bhupalsagar; and (iii) north of Dhani. Selective geochemical sampling and resistivity profiling carried out in these favourable zones indicated the presence of geochemical anomalies (anomalous concentrations of Zn and Cu) and low/moderate resistivity zones, respectively. Recent drilling carried out by the Department of Mines and Geology (DMG), Rajasthan, at about 2.5 km north of Rawan ka Khera (one of the predicted favourable zones) indicated evidence of Cu mineralization at a depth of about 70 m.     

Neural network and GMM based feature mappings for consonant–vowel recognition in emotional environment

In this work, we propose a mapping function based feature transformation framework for developing consonant–vowel (CV) recognition system in the emotional environment. An effective way of conveying messages is by expressing emotions during human conversations. The characteristics of CV units differ from one emotion to other emotions. The performance of existing CV recognition systems is degraded in emotional environments. Therefore, we have proposed mapping functions based on artificial neural network and GMM models for increasing the accuracy of CV recognition in the emotional environment. The CV recognition system has been explored to transform emotional features to neutral features using proposed mapping functions at CV and phone levels to minimize mismatch between training and testing environments. Vowel onset and offset points have been used to identify vowel, consonant and transition segments. Transition segments are identified by considering initial 15% speech samples between vowel onset and offset points. The average performance of CV recognition system is increased significantly using feature mapping technique at phone level in three emotional environments (anger, happiness, and sadness).     

Attribute-based key-insulated signature for boolean formula

In order to minimize the impact of secret signing key exposure in attribute-based signature scenario, we construct an attribute-based key-insulated signature (ABKIS) scheme for expressive monotone boolean function access structures utilizing only four pairing operations in verification process and making the signature length constant, that is, the number of pairings required for signature verification and the size of signature are independent of the size of attribute set participated in the respective process. The (strong) key-insulated selective security of our ABKIS scheme is reduced to the computational Diffie–Hellman Exponent problem without using any random oracles. The proposed construction attains signer privacy, which is a fundamental requirement of the signature schemes in the attribute-based setting.