Bayesian inference of a negative quantity from positive measurement results

In this paper the Bayesian analysis is applied to assign a probability density to the value of a quantity having a definite sign. This analysis is logically consistent with the results, positive or negative, of repeated measurements. Results are used to estimate the atom density shift in a caesium fountain clock. The comparison with the classical statistical analysis is also reported and the advantages of the Bayesian approach for the realization of the time unit are discussed.Comment: 10 pages, 4 figures, submitted to Metrologi

The Big Brown Bear

https://digitalcommons.library.umaine.edu/mmb-vp/5536/thumbnail.jp

Adapting Hybrid ANN/HMM to Speech Variations

A technique is proposed for the adaptation of automatic speech recognition systems using Hybrid models combining Artificial Neural Networks with Hidden Markov Models. We investigated in this paper the extension of the classical approach consisting in applying linear transformations not only to the input features, but also to the outputs of the internal layers. The motivation is that the outputs of an internal layer represent a projection of the input pattern into a space where it should be easier to learn the classification or transformation expected at the output of the network. To reduce the risk that the network focuses on new data only, loosing its generalization capability (catastrophic forgetting), an original solution, Conservative Training is proposed. We illustrate the problem of catastrophic forgetting using an artificial test-bed, and apply our techniques to a set of adaptation tasks in the domain of Automatic Speech Recognition (ASR) based on Artificial Neural Networks. We report on the adaptation potential of different techniques, and on the generalization capability of the adapted networks. The results show that the combination of the proposed approaches mitigates the catastrophic forgetting effects, and always outperforms the use of the classical linear transformation in the feature space. 1

Adaptation of Hybrid ANN/HMM Models using Linear Hidden Transformations and Conservative Training

International audienceA technique is proposed for the adaptation of automatic speech recognition systems using Hybrid models combining Artificial Neural Networks with Hidden Markov Models. The application of linear transformations not only to the input features, but also to the outputs of the internal layers is investigated. The motivation is that the outputs of an internal layer represent a projection of the input pattern into a space where it should be easier to learn the classification or transformation expected at the output of the network. A new solution, called Conservative Training, is proposed that compensates for the lack of adaptation samples in certain classes. Supervised adaptation experiments with different corpora and for different adaptation types are described. The results show that the proposed approach always outperforms the use of transformations in the feature space and yields even better results when combined with linear input transformations