A method of speaker adaptation for continuous density hidden Markov models (HMMs) is presented. An initial speaker-independent system is adapted to improve the modelling of a new speaker by updating the HMM parameters. Statistics are gathered from the available adaptation data and used to calculate a linear regressionbased transformation for the mean vectors. The transformation matrices are cal...

We present a new way to take advantage of the dis-criminative power of Learning Vector Quantization in combination with continuous density hidden Markov models. This is based on viewing LVQ as a non-linear feature transformation. Class-wise quantization errors of LVQ are modeled by continuous density HMMs, whereas the practice in the literature regarding LVQ/HMM hybrids is to use LVQ-codebooks ...

Training of continuous density hidden Markov models (CDHMMs) is usually time-consuming and tedious due to the large number of model parameters involved. Recently we proposed a new derivative of CDHMM, the sub-space distribution clustering hidden Markov model (SD-CHMM) which tie CDHMMs at the ner level of subspace distributions, resulting in many fewer model parameters. An SDCHMM training algori...

It generally takes a long time and requires a large amount of speech data to train hidden Markov models for a speech recognition task of a reasonably large vocabulary. Recently, we proposed a compact acoustic model called “subspace distribution clustering hidden Markov model” (SDCHMM) with an aim to save some of the training effort. SDCHMMs are derived from tying continuous density hidden Marko...

In this paper we present an alternative to hidden Markov models for the recognition of image sequences. The approach is based on a stochastic version of recurrent neural networks, which we call diffusion networks. Contrary to hidden Markov models, diffusion networks operate with continuous state dynamics, and generate continuous paths. This aspect that may be beneficial in computer vision tasks...

The Student’s-t hidden Markov model (SHMM) has been recently proposed as a robust to outliers form of conventional continuous density hidden Markov models, trained by means of the expectation-maximization algorithm. In this paper, we derive a tractable variational Bayesian inference algorithm for this model. Our innovative approach provides an efficient and more robust alternative to EM-based m...

This paper explores a framework for recognition of image sequences using partially observable stochastic differential equation (SDE) models. Monte-Carlo importance sampling techniques are used for efficient estimation of sequence likelihoods and sequence likelihood gradients. Once the network dynamics are learned, we apply the SDE models to sequence recognition tasks in a manner similar to the ...

Training of continuous density hidden Markov models (CDHMMs) is usually time-consuming and tedious due to the large number of model parameters involved. Recently we proposed a new derivative of CDHMM, the subspace distribution clustering hidden Markov model (SDCHMM) which tie CDHMMs at the ner level of subspace distributions, resulting in many fewer model parameters. An SDCHMM training algorith...

This paper deals with a recent statistical model based on fuzzy Markov random chains for image segmentation, in the context of stationary and non-stationary data. On one hand, fuzzy scheme takes into account discrete and continuous classes through the modeling of hidden data imprecision and on the other hand, Markovian Bayesian scheme models the uncertainty on the observed data. A non-stationar...