Chasing the metric: Smoothing learning algorithms for keyword detection

In this paper we propose to directly optimize a discrete objective function by smoothing it, showing it is both effective at enhancing the figure of merit that we are interested in while keeping the overall complexity of the training procedure unaltered. We looked at the task of keyword detection with data scarcity (e.g., for languages for which we do not have enough data), and found it useful to optimize the Actual Term Weighted Value (ATWV) directly. In particular, we were able to automatically set the detection threshold while improving ATWV by more than 1% using a computationally cheap method based on a smoothed ATWV on both single systems and for system combination. Furthermore, we did study additional features to refine keyword candidates which were easy to optimize thanks to the same techniques, and improved ATWV by an additional 1%. The advantage of our method with respect to others is that, since we can use continuous optimization techniques, it does not impose a limit in the number of parameters that other discrete optimization techniques exhibit.