Design of Hierarchical Classifier to Improve Speech Emotion Recognition

Automatic Speech Emotion Recognition(SER)is used to recognize emotion from speech automatically.Speech Emotion recognition is working well in a laboratory environment but real-time emotion recognition has been influenced by the variations in gender,age,the cultural and acoustical background of the speaker.The acoustical resemblance between emotional expressions further increases the complexity of recognition.Many recent research works are concentrated to address these effects individually.Instead of addressing every influencing attribute individually,we would like to design a system,which reduces the effect that arises on any factor.We propose a two-level Hierarchical classifier named Interpreter of responses(IR).Thefirst level of IR has been realized using Support Vector Machine(SVM)and Gaussian Mixer Model(GMM)classifiers.In the second level of IR,a discriminative SVM classifier has been trained and tested with meta information offirst-level classifiers along with the input acoustical feature vector which is used in primary classifiers.To train the system with a corpus of versatile nature,an integrated emotion corpus has been composed using emotion samples of 5 speech corpora,namely;EMO-DB,IITKGP-SESC,SAVEE Corpus,Spanish emotion corpus,CMU's Woogle corpus.The hierarchical classifier has been trained and tested using MFCC and Low-Level Descriptors(LLD).The empirical analysis shows that the proposed classifier outperforms the traditional classifiers.The proposed ensemble design is very generic and can be adapted even when the number and nature of features change.Thefirst-level classifiers GMM or SVM may be replaced with any other learning algorithm.

Monitoring urban land cover and vegetation change by multi-temporal remote sensing information

In order to analyze changes in human settlement in Xuzhou city during the past 20 years, changes in land cover and vegetation were investigated based on multi-temporal remote sensing Landsat TM images. We developed a hierarchical classifier system that uses different feature inputs for specific classes and conducted a classification post-processing approach to improve its accuracy. From our statistical analysis of changes in urban land cover from 1987 to 2007, we conclude that built-up land areas have obviously increased, while farmland has seen in a continuous loss due to urban growth and human activities. A NDVI difference approach was used to extract information on changes in vegetation. A false change information elimination approach was developed based on prior knowledge and statistical analysis. The areas of vegetation cover have been in continuous decline over the past 20 years, although some measures have been adopted to protect and maintain urban vegetation. Given the stability of underground coal exploitation since 1990s, urban growth has become the major driving force in vegetation loss, which is different from the vegetation change driven by coal exploitation mainly before 1990.

Improved modulation format identification based on Stokes parameters using combination of fuzzy c-means and hierarchical clustering in coherent optical communication system

In this Letter, we develop the Stokes space-based method for modulation format identification by combing power spectral density and a cluster analysis to identify quadrature amplitude modulation（QAM） and phase-shift keying（PSK） signals. Fuzzy c-means and hierarchical clustering algorithms are used for the cluster analysis.Simulations are conducted for binary PSK, quadrature PSK, 8PSK, 16-QAM, and 32-QAM signals. The results demonstrate that the proposed technique can effectively classify all these modulation formats, and that the method is superior in lowering the threshold of the optical signal-to-noise ratio. Meanwhile, the proposed method is insensitive to phase offset and laser phase noise.