Dynamic Bayesian networks for multi-band automatic speech recognition

Automatic Speech Recognition using Dynamic Bayesian Networks

New ideas to improve automatic speech recognition have been proposed that make use of context user information such as gender, age and dialect. To incorporate this information into a speech recognition system a new framework is being developed at the mmi department of the ewi faculty at the Delft University of Technology. This toolkit is called Gaia and makes use of Dynamic Bayesian networks. I...

Automatic Speech Recognition using Pitch Information in Dynamic Bayesian Networks

Speech Recognition with Dynamic Bayesian Networks

Dynamic Bayesian networks (DBNs) are a useful tool for representing complex stochastic processes. Recent developments in inference and learning in DBNs allow their use in real-world applications. In this paper, we apply DBNs to the problem of speech recognition. The factored state representation enabled by DBNs allows us to explicitly represent long-term articulatory and acoustic context in add...

Probabilistic modeling with Bayesian networks for automatic speech recognition

Bayesian networks are an extremely general prob-abilistic modeling framework, and are increasingly being applied to complex real-world problems. In this paper, we describe the use of a Bayesian network system in large vocabulary isolated word recognition. We brieey review the algorithms and network structures used, and present results showing that signiicant improvements in word error rate resu...

Mixed Bayesian Networks with Auxiliary Variables for Automatic Speech Recognition

In standard automatic speech recognition (ASR), hidden Markov models (HMMs) calculate their emission probabilities by an artificial neural network (ANN) or a Gaussian distribution conditioned only upon the hidden state variable. Recent work [12] showed the benefit of conditioning the emission distributions also upon a discrete auxiliary variable, which is observed in training and hidden in reco...