context-dependent modeling is a well-known approach to increase modeling accuracy in continuous speech recognition. the most common way to implement this approach is via triphone modeling. nevertheless, the large number of such models results in several problems in model training, whilst the robust training of such models is often hardly obtained. one approach to solve this problem is via param...

the geometric distribution of states duration is one of the main performance limiting assumptions of hidden markov modeling of speech signals. stochastic segment models, generally, and segmental hmm, specifically, overcome this deficiency partly at the cost of more complexity in both training and recognition phases. in this paper, a new duration modeling approach is presented. the main idea of ...

Today’s state-of-the-art speech recognition systems typically use continuous density hidden Markov models with mixture of Gaussian distributions. Such speech recognition systems have problems; they require too much memory to run, and are too slow for large vocabulary applications. Two approaches are proposed for the design of compact acoustic models, namely, subspace distribution clustering hid...

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...

We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test pro...

Continuous density hidden Markov models (CD-HMMs) are an essential component of modern systems for automatic speech recognition (ASR). These models assign probabilities to the sequences of acoustic feature vectors extracted by signal processing of speech waveforms. In this chapter, we investigate a new framework for parameter estimation in CD-HMMs. Our framework is inspired by recent parallel t...

In this paper we describe a speaker-cluster normalization algorithm that we applied to both gendernormalization and speaker-normalization. To achieve parameter sharing the acoustic space is partitioned into classes. A maximum likelihood approach has been proposed under which the delta between the distribution mean and its corresponding acoustic class is mostly speaker-independent, whereas the m...

Hidden Markov models [2] (HMMs) have been successfully applied to Bioinformatics such as gene finding, remote homology detection and secondary structure prediction. On the other hand, continuous density HMMs have been widely used in the field of speech recognition. Though continuous density HMMs were not applied to Bioinformatics so far, they may also be useful in Bioinformatics. Currently, we ...

As one of the most powerful smoothing techniques, deleted interpolation has been widely used in both discrete and semi-continuous hidden Markov model (HMM) based speech recognition systems. For continuous HMMs, most smoothing techniques are carried out on the parameters themselves such as Gaussian mean or covariance parameters. In this paper, we propose to smooth the probability density values ...