We explore active learning (AL) for improving the accuracy of new domains in a natural language understanding (NLU) system. We propose an algorithm called Majority-CRF that uses an ensemble of classification models to guide the selection of relevant utterances, as well as a sequence labeling model to help prioritize informative examples. Experiments with three domains show that Majority-CRF achieves 6.6%-9% relative error rate reduction compared to random sampling with the same annotation budget, and statistically significant improvements compared to other AL approaches. Additionally, case studies with human-in-the-loop AL on six new domains show 4.6%-9% improvement on an existing NLU system.

As users become more accustomed to using their mobile devices to organize and schedule their lives, there is more of a demand for applications that can make that process easier. Automatic speech recognition technology has already been developed to enable essentially unlimited vocabulary in a mobile setting. Understanding the words that are spoken is the next challenge. In this paper, we describe efforts to develop a dataset and classifier to recognize named entities in speech. Using sets of both real and simulated data, in conjunction with a very large set of real named entities, we created a challenging corpus of training and test data. We use these data to develop a classifier to identify names and locations on a word-by-word basis. In this paper, we describe the process of creating the data and determining a set of features to use for named entity recognition. We report on our classification performance on these data, as well as point to future work in improving all aspects of the system.

In the framework of the EU funded project TC-STAR (Technology and Corpora for Speech to Speech Translation),research on TTS aims on providing a synthesized voice sounding like the source speaker speaking the target language. To progress in this direction, research is focused on naturalness, intelligibility, expressivity and voice conversion both, in the TC-STAR framework. For this purpose, specifications on large, high quality TTS databases have been developed and the data have been recorded for UK English, Spanish and Mandarin. The development of speech technology in TC-STAR is evaluation driven. Assessment of speech synthesis is needed to determine how well a system or technique performs in comparison to previous versions as well as other approaches (systems & methods). Apart from testing the whole system, all components of the system will be evaluated separately. This approach grants better assesment of each component as well as identification of the best techniques in the different speech synthesisprocesses.This paper describes the specifications of Language Resources for speech synthesis and the specifications for evaluation of speech synthesis activities.