Transfer Learning for Speech and Language Processing

Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.Comment: 13 pages, APSIPA 201

Leading indicators of country risk and currency crises: the Asian experience

Most emerging capital markets in recent years adopted a system that narrowly pegs their currencies’ exchange rates to the U.S. dollar. While such a system has a number of advantages, it makes a country vulnerable to shocks in mobile international capital markets and can lead to reactive strategies that can drive the country into a currency crisis and inflationary recession. ; This article aims to construct an early warning system for international currency crises using financial variables reflecting investors’ expectations and banking distress, which are highly sensitive to changes in the economic environment. The authors use a dynamic factor model that switches between two regimes—representing periods of relative calmness and periods prone to currency crises—to construct leading indicators of country risk and currency crises. ; The method is applied to evaluate the model’s in-sample and out-of-sample performance in anticipating currency crises in the last two decades in Thailand, Indonesia, and Korea. The model successfully produces early signals of these crises, particularly the most severe one, which occurred in 1997. ; The study’s success in signaling future currency crises in real time demonstrates that the model’s “country risk” indicators can be informative tools that allow central banks to take preemptive counterpolicy measures to avoid a crisis or mitigate its severity.