Modelling reassurances of clinicians with hidden Markov models

Modelling Intonational Structure Using Hidden Markov Models

A method is introduced for using hidden Markov models (HMMs) to model intonational structure. HMMs are probabilistic and can capture the variability in structure which previous finite state network models lack. We show how intonational tunes can be modelled by separate HMMs and how HMMs can be used in a recognition system to automatically determine the tune type of an utterance.

Space Time Modelling of Precipitation Using Hidden Markov Models

In this paper, we propose a new hidden Markov model (HMM) for the space-time evolution of daily rainfall. The hidden Markov chain represents the different meteorological regimes (“weather types”) and it is assumed that this variable explains the dynamics of the precipitation. The spatial structure within hidden weather types is modelled by censored power-transformed Gaussian distributions. It p...

Acoustic modelling for speech recognition: Hidden Markov models and beyond?

Moving Toward High Precision Dynamical Modelling in Hidden Markov Models

Hidden Markov Model (HMM) is often regarded as the dynamical model of choice in many fields and applications. It is also at the heart of most state-of-theart speech recognition systems since the 70’s. However, from Gaussian mixture models HMMs (GMMHMM) to deep neural network HMMs (DNN-HMM), the underlying Markovian chain of state-of-the-art models did not changed much. The “left-to-right” topol...

Isadora | a Speech Modelling Network Based on Hidden Markov Models

In this paper we present the ISADORA system which provides highly exible speech recognition based on HMM technology together with an hierarchical representation of speech units. Markov model topologies, subword unit inventories, regular grammars expressed in nite-state or phrase structure style, and even the analysis tasks themselves are explicitly represented by the nodes of a large speech uni...