Recognizing human emotions through vocal channel has gained increased attention recently. In this paper, we study how used features, and classifiers impact recognition accuracy of emotions present in speech. Four emotional states are considered for classification of emotions from speech in this work. For this aim, features are extracted from audio characteristics of emotional speech using Linea...

Accent classification technologies directly influence the performance of speech recognition. Currently, two models are used for accent detection namely: Hidden Markov Model (HMM) and Artificial Neural Networks (ANN). However, both models have some drawbacks of their own. In this paper, we use Support Vector Machine (SVM) to detect different speakers’ accents. To examine the performance of SVM, ...

Widely Speech Signal Processing has not been used much in the field of electronics and computers due to the complexity and variety of speech signals and sounds with the advent of new technology. However, with modern processes, algorithms, and methods which can proc Demand for speech recognition technology is expected to their mobile phones as all purpose lifestyle devices. In this paper, an imp...

Development of Malayalam speech recognition system is in its infancy stage; although many works have been done in other Indian languages. In this paper we present the first work on speaker independent Malayalam isolated speech recognizer based on PLP (Perceptual Linear Predictive) Cepstral Coefficient and Hidden Markov Model (HMM). The performance of the developed system has been evaluated with...

This paper introduces emotion recognition for facial expression images using Hidden Markov Model (HMM). Firstly, facial expression images are transformed using discrete cosine transformation and feature is extraction; then HMMs of facial expression images are constructed, and the observation vectors are generated using sub-window, hence, process of emotion recognition for facial expression imag...

In this paper, we model a stock dynamic using Hidden Markov Model (HMM) where weekly return is normally distributed with mean and variance depending on the hidden random variable representing the stock trend. Using Expectation-Maximization algorithm, we estimate all the parameters and design a simple trading strategy based on it. We then compare its performance with Buy and Hold and Resistance ...

In this paper we formally expand hidden Markov models (HMM) by symbol emissions in state changes. These expanded hidden Markov models (eHMM) can contain more information than original HMM with the same number of states. This is a necessary step towards the definition of hidden non-Markovian models on the basis of discrete stochastic models. These are most of the time event driven, which makes i...

Wi-Fi fingerprinting is one of the methods that are widely used to provide Location Based Services (LBS). Gaussian, or a mixture Gaussians, preferred model by for LBS. Nevertheless, Received Signal Strength Intensity (RSSI) histograms skewed, and Gaussian not well suited modeling data when their histogram skewed. In addition, another important characteristic present in RSSI temporal series auto...

In this work, we conducted an empirical comparative study of the performance text-independent speaker verification in emotional and stressful environments. This work combined deep models with shallow architecture, which resulted novel hybrid classifiers. Four distinct were utilized: neural network-hidden Markov model (DNN-HMM), network-Gaussian mixture (DNN-GMM), Gaussian model-deep network (GM...

In the present paper, a hidden-semi Markov model (HSMM) based speech synthesis system is proposed. In a hidden Markov model (HMM) based speech synthesis system which we have proposed, rhythm and tempo are controlled by state duration probability distributions modeled by single Gaussian distributions. To synthesis speech, it constructs a sentence HMM corresponding to an arbitralily given text an...