Automatic segmentation of text strings, in particular entity names, into structured records is often needed for efficient information retrieval, analysis, mining, and integration. Hidden Markov Model (HMM) has been shown as the state of the art for this task. However, previous work did not take into account the synonymy of words and their abbreviations, or possibility of their misspelling. In t...

Different techniques are available for the prediction of stock market. Very popular some of these are Neural Network, Data Mining, Hidden Markov Model(HMM) And Neuro-Fuzzy system. From these Neural Network and Neuro-Fuzzy Systems are the most leading machine learning techniques in stock market index prediction area. Other traditional methods do not cover all possible relation of stock price mov...

In music information retrieval (MIR), beat tracking is one of the most fundamental tasks. To obtain this critical component from rhythmic signals, a previous system hidden Markov model (HMM) with recurrent neural network (RNN) observer was developed. Although frequency quite stable, existing HMM based methods do not take feature into account. Accordingly, states in these HMM-based are redundant...

In the last few years we have been experimenting with an automatic phonetic segmentation and labeling system based on a modified HMM phonetic recognizer followed by a local phonetic boundary refinement system. During this period we have used different approaches for the local refinement, including fuzzy rules and neural networks. In this paper we present a unified framework for the local refine...

Robot skill acquisition can be conceptualized as a task of identifying patterns in a spatio-temporal sensory feature space. Skills can be mathematically learned through identifying mappings from sensory signals into Qualitative States (QS), construction of a QS skill automaton, and the detection of motor or output commands that transition the model from the present QS to the next QS. In this pa...

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...

Abstract In this paper, we propose a supervised single-channel speech enhancement method that combines Kullback-Leibler (KL) divergence-based non-negative matrix factorization (NMF) and hidden Markov model (NMF-HMM). With the integration of HMM, temporal dynamics information signals can be taken into account. This includes training stage an stage. stage, sum Poisson distribution, leading to KL ...