In this paper we compare the performance of speech recognition systems based on hidden Markov models (HMM) with quantized parameters (qHMMs) and subspace distribution clustering hidden Markov models (SDCHMMs). Both of these HMM types provide similar performance as continuous density HMMs, but with significantly reduced memory requirements (approximately 90% less memory was needed to store the H...

This paper addresses the problem of robust speech recognition in noisy conditions in the framework of hidden Markov models (HMMs) and missing feature techniques. It presents a new statistical approach to detection and estimation of unreliable features based on a probabilistic measure and Gaussian mixture model (GMM). In the estimation process, the GMM is compensated using parameters of the stat...

This presentation is intended to be a short course on inference and filtering in hidden Markov models (HMMs) and state space models. These models comprise a hidden (unobserved) Markov chain (either with discrete or continuous state space) that governs the distribution of an observed stochastic process. An example of a HMM is a regime-switching model for stock returns in which the stock return d...

Input-output hidden Markov models (IOHMM) are conditional hidden Markov models in which the emission (and possibly the transition) probabilities can be conditioned on an input sequence. For example, these conditional distributions can be linear, logistic, or nonlinear (using for example multilayer neural networks). We compare the generalization performance of several models which are special ca...

State of the art automatic speech recognition system uses Mel frequency cepstral coefficients as feature extractor along with Gaussian mixture model for acoustic modeling but there is no standard value to assign number of mixture component in speech recognition process.Current choice of mixture component is arbitrary with little justification. Also the standard set for European languages can no...

OBSERVATION PROBABILITY AND STATE DURATION-DEPENDENT OBSERVATION PROBABILITY (NO. OF TEST WORDS WAS 2810) I I I hameter I P ~ D D D S ~ ~ I NUMBERS OF MIXTURES BETWEEN STATE DURATION-INDEPENDENT Acoust, Speech, Signal Processing, vol. ASSP-33, pp 587-594, June 1985. [7] Y. J. Chung and C. K. Un, “Use of different numbers of mixtures in continuous density hidden Markov models,” IEE Electron. Let...

Due to the effective role of Markov models in customer relationship management (CRM), there is a lack of comprehensive literature review which contains all related literatures. In this paper the focus is on academic databases to find all the articles that had been published in 2011 and earlier. One hundred articles were identified and reviewed to find direct relevance for applying Markov models...