In this paper, we compare both discriminative and generative parameter learning on both discriminatively and generatively structured Bayesian network classifiers. We use either maximum likelihood (ML) or conditional maximum likelihood (CL) to optimize network parameters. For structure learning, we use either conditional mutual information (CMI), the explaining away residual (EAR), or the classification rate (CR) as objective functions. Experiments with the naive Bayes classifier (NB), the tree augmented naive Bayes classifier (TAN), and the Bayesian multinet have been performed on 25 data sets from the UCI repository (Merz et al., 1997) and from (Kohavi & John, 1997). Our empirical study suggests that discriminative structures learnt using CR produces the most accurate classifiers on almost half the data sets. This approach is feasible, however, only for rather small problems since it is computationally expensive. Discriminative parameter learning produces on average a better classifier than ML parameter learning. 1.

We present an approach to general multi-stream recognition utilizing multi-tape finite-state transducers (FSTs). The approach is novel in that each of the multiple “streams ” of features can represent either a sequence (e.g., fixed- or variable-rate frames) or a directed acyclic graph (e.g., containing hypothesized phonetic segmentations). Each transition of the multi-tape FST specifies the models to be applied to each stream and the degree of feature stream asynchrony to allow. We show how this framework can easily represent the 2-stream variable-rate landmark and segment modeling utilized by our baseline SUMMIT speech recognizer. We present experiments merging standard hidden Markov models (HMMs) with landmark models on the Wall Street Journal speech recognition task, and find that some degree of asynchrony can be critical when combining different types of models. We also present experiments performing audio-visual speech recognition on the AV-TIMIT task. 1.

Abstract — This paper introduces multi-rate coupled hidden Markov models (multi-rate HMMs for short) for multiscale modeling of nonstationary processes, extending traditional HMMs from single to multiple time scales with hierarchical representations of the process state and observations. Scales in the multi-rate HMMs are organized in a coarse-to-fine manner with Markov conditional independence assumptions within and across scales, allowing for a parsimonious representation of both shortand long-term context and temporal dynamics. Efficient inference and parameter estimation algorithms for the multi-rate HMMs are given, which are similar to the analogous algorithms for HMMs. The model is applied to the classification of tool wear in titanium milling, for which acoustic emissions exhibit multiscale dynamics and long-range dependence. Experimental results show that the multi-rate extension outperforms HMMs in terms of both wear prediction accuracy and confidence estimation. Index Terms — Hidden Markov model, multi-rate hidden Markov model, multiscale statistical modeling, confidence, tool