AnA/B-Testevaluates the impact of a new technology by running it in a real production environment and testing its performance on a set of items. Recent development efforts aroundA/B-Testsrevolve around dynamic allocation. They allow for quicker determination of the best variation (A or B), thus saving money for the user. However, dynamic allocation by traditional methods requires certain assumptions, which are not always valid in reality. This is often due to the fact that the populations being tested are not homogeneous. This article reports on a new reinforcement learning methodology which has been deployed by the commercialA/B-Testplatform AB Tasty. We provide a new method that not only builds homogeneous groups of users, but also allows the best variation for these groups to be found in a short period of time. This article provides numerical results on AB Tasty's data, in addition to public datasets, tha demonstrate an improvement over traditional methods.

LIDIAP

This paper describes a multimodal approach for speaker verification. The system consists of two classifiers, one using visual features, the other using acoustic features. A lip tracker is used to extract visual information from the speaking face which provides shape and intensity features. We describe an approach for normalizing and mapping different modalities onto a common confidence interval. We also describe a novel method for integrating the scores of multiple classifiers. Verification experiments are reported for the individual modalities and for the combined classifier. The integrated system outperformed each sub-system and reduced the false acceptance rate of the acoustic sub-system from 2.3% to 0.5%.

The goal is to improve recognition rate by optimisation of Mel Frequency Cepstral Coecients (MFCCs): modications concern the time-frequency representations used to estimate these coecients.There are many w a ys to obtain a spectrum out of a signal which dier in the method itself (Fourier, Wavelets,...), and in the normalisation.We show here that we can obtain noise resistant cepstral coecients, for speaker independent connected word recognition.The recognition system is based on a continuous whole word hidden Markov model.An error reduction rate of approximately 50% is achieved with word models.