Migration Industries and the State: Guestwork Programs in East Asia

Studies of migration industries have demonstrated the critical role that border-spanning businesses play in international mobility. To date, most research has focused on meso-level entrepreneurial initiatives that operate in a legal gray area under a state that provides an environment for their growth or decline. Extending this work, the present article advances a taxonomy of the ways states partner with migration industries based on the nature of their relationship (formal or informal) and the type of actor involved (for-profit or non-profit). The analysis focuses on low-paid temporary migrant work programs — schemes that require substantial state involvement to function — and examines cases from the East Asian democracies with strong economies that have become net importers of migrants: Taiwan, Japan, and South Korea. The conclusion, incorporating cases beyond Asia, explicates the properties and limits of each arrangement based on the degree of formality and importance of profit

Multilingual Speech Recognition

The speech-to-speech translation system Verbmobil requires a multilingual setting. This consists of recognition engines in the three languages German, English and Japanese that run in one common framework together with a language identification component which is able to switch between these recognizers. This article describes the challenges of multilingual speech recognition and presents different solutions to the problem of the automatic language identification task. The combination of the described components results in a flexible and user-friendly multilingual spoken dialog system

Blind Suppression of Nonstationary Diffuse Acoustic Noise Based on Spatial Covariance Matrix Decomposition

International audienceWe propose methods for blind suppression of nonstationary diffuse noise based on decomposition of the observed spatial covariance matrix into signal and noise parts. In modeling noise to regularize the ill-posed decomposition problem, we exploit spatial invariance (isotropy) instead of temporal invariance (stationarity). The isotropy assumption is that the spatial cross-spectrum of noise is dependent on the distance between microphones and independent of the direction between them. We propose methods for spatial covariance matrix decomposition based on least squares and maximum likelihood estimation. The methods are validated on real-world recordings