In this paper, we first consider the parameter estimation of a multivariate random process distribution using multivariate Gaussian mixture law. The labels of the mixture are allowed to have a general probability law which gives the possibility to modelize a temporal structure of the process under study. We generalize the case of univariate Gaussian mixture in [1] to show that the likelihood is...

Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very ...

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...

An acoustic model for an embedded speech recognition system must exhibit two desirable features; the ability to minimize the performance degradation in recognition, while solving the memory problem under the constraint of limited system resources. Moreover, for general speech recognition tasks, context dependent models such as state-clustered tri-phones are used to guarantee the high recognitio...

In this letter, we propose a new acoustic modelling approach for automatic speech recognition based on probabilistic linear discriminant analysis (PLDA), which is used to model the state density function for the standard hidden Markov models (HMMs). Unlike the conventional Gaussian mixture models (GMMs) where the correlations are weakly modelled by using the diagonal covariance matrices, PLDA c...

We present a generative, probabilistic model that decomposes an image into reflectance and shading components. The proposed approach uses a Dirichlet process Gaussian mixture model where the mean parameters evolve jointly according to a Gaussian process. In contrast to prior methods, we eliminate the Retinex term and adopt more general smoothness assumptions for the shading image. Markov chain ...

Automatic speech recognition (ASR) systems usually consist of an acoustic model and a language model. This paper describes a technique of an efficient deployment of the acoustic model parameters. The acoustic model typically utilizes Continuous Density Hidden Markov Models (CDHMM). The output probability of a particular CDHMM state is represented by a Gaussian mixture density with a diagonal co...

This paper reports on the participation of FBK in the IWSLT 2014 evaluation campaign for Automatic Speech Recognition (ASR), which focused on the transcription of TED talks. The outputs of primary and contrastive systems were submitted for three languages, namely English, German and Italian. Most effort went into the development of the English transcription system. The primary system is based o...