Traditionally, human activity recognition has been achieved mainly by the statistical pattern recognition techniques such as the Nearest Neighbor Rule (NNR), and the state-space methods, e.g. the Hidden Markov Model (HMM). This paper proposes three novel approaches – the use of the Elman Network (EN) and two hybrids of Neural Network (NN) and HMM, i.e. HMM-NN and NN-HMM, to recognize ten simple...

Discrete Hidden Markov Model (HMM) and hybrid of Neural Network (NN) and HMM are popular methods in handwritten word recognition system. In the hybrid system, NN is used for character level recognition while HMM is used for producing word score based on the probability of the hypothesized characters combined. All reported results shows better recognition for the hybrid system due to better disc...

This paper addresses the problem of capturing the dynamics for exemplar-based recognition systems. Traditional HMM provides a probabilistic tool to capture system dynamics and in exemplar paradigm, HMM states are typically coupled with the exemplars. Alternatively, we propose a non-parametric HMM approach that uses a discrete HMM with arbitrary states (decoupled from exemplars) to capture the d...

Recent advances in automatic speech recognition (ASR) have achieved accuracy levels comparable to human transcribers, which led researchers debate if the machine has reached performance. Previous work focused on English language and modular hidden Markov model-deep neural network (HMM–DNN) systems. In this paper, we perform a comprehensive benchmarking for end-to-end transformer ASR, HMM–DNN (H...

Hexamethylmelamine (HMM) is metabolized by rat hepatic microsomal preparat ions to reactive species which covalent ly bind to microsomal protein and to calf thymus DNA added to microsomal incubation mixtures. Covalent binding to macromolecules is dependent on the presence of molecular oxygen and reduced nicot inamide adenine dinucleot ide phosphate and is catalyzed by cytochrome P-450 monooxyge...

The partial specific adiabatic compressibilities of myosin subfragment-1 (S1) and heavy meromyosin (HMM) of skeletal muscle in solution were determined by measuring the density and the sound velocity of the solution. The partial specific volumes of S1 and HMM were 0.713 and 0.711 cm3/g, respectively. The partial specific adiabatic compressibilities of S1 and HMM were 4.2 x 10(-12) and 2.9 x 10(...

Hidden Markov Models (HMM) are currently widely used in Automatic Speech Recognition (ASR) as being the most effective models. Yet, they sometimes pose some problems of discrimination. The hybridization of Artificial Neural Networks (ANN) in particular Multi Layer Perceptrons (MLP) with HMM is a promising technique to overcome these limitations. In order to ameliorate results of recognition sys...

Automatic detection of phoneme boundaries is an important sub-task in building speech processing applications, especially text-to-speech synthesis (TTS) systems. The main drawback of the Gaussian mixture model hidden Markov model (GMMHMM) based forced-alignment is that the phoneme boundaries are not explicitly modeled. In an earlier work, we had proposed the use of signal processing cues in tan...

Hexamethylmelamine (HMM) is metabolized by rat hepatic microsomal preparat ions to reactive species which covalent ly bind to microsomal protein and to calf thymus DNA added to microsomal incubation mixtures. Covalent binding to macromolecules is dependent on the presence of molecular oxygen and reduced nicot inamide adenine dinucleot ide phosphate and is catalyzed by cytochrome P-450 monooxyge...

Currently, the hidden Markov model (HMM) is the predominant model studied and used for speech recognition. There has been undeniable progress in speech recognition through the study of HMM but the huge gap that exists between user’s expectation and progress is also undeniable. There are essentially two limitation with the HMM: (1) The Markovian structure in HMM leads to limitation in what it ca...