Exploiting variable-width features in large vocabulary speech recognition

The use of variable-width features (prosodics, broad structural information etc.) in large vocabulary speech recognition systems is discussed. Although the value of this sort of information has been recognized in the past, previous approaches have not been widely used in speech systems because either they have not been robust enough for realistic, large vocabulary tasks or they have been limited to certain recognizer architectures. A framework for the use of variable-width features is presented which employs the N-Best algorithm with the features being applied in a post-processing phase. The framework is flexible and widely applicable, giving greater scope for exploitation of the features than previous approaches. Large vocabulary speech recognition experiments using TIMIT show that the application of variable-width features has potential benefits.

The silicon backplane design for an LCOS polarization-insensitive phase hologram SLM

Polarization-insensitivity is achieved in a reflective spatial light modulator by laying a quarter-wave plate (QWP) at the incident wavelength directly over the mirror pixels of a silicon backplane, and forming a nematle Fréedrickcz cell over the QWP to modulate the reflected phase. To achieve the highest drive voltage from the available silicon process, a switched voltage common front electrode design is described, with variable amplitude square wave drive to the pixels to maintain constant root-mean-square drive and minimize phase fluctuations during the dc balance refresh cycle. The silicon has been fabricated and liquid-crystal-on-silicon cells both with and without the QWP assembled; applications include optically transparent switches for optical networks, beam steering for add-drop multiplexers for wavelength-division- multiplexing telecommunications, television multicast, and holographic projection.

Multi-dimensional conditional moment closure modelling applied to a heavy-duty common-rail diesel engine

A multi-dimensional combustion code implementing the Conditional Moment Closure turbulent combustion model interfaced with a well-established RANS two- phase flow field solver has been employed to study a broad range of operating conditions for a heavy duty direct-injection common-rail Diesel engine. These conditions include different loads (25%, 50%, 75% and full load) and engine speeds (1250 and 1830 RPM) and, with respect to the fuel path, different injection timings and rail pressures. A total of nine cases have been simulated. Excellent agreement with experimental data has been found for the pressure traces and the heat release rates, without adjusting any model constants. The chemical mechanism used contains a detailed NOx sub-mechanism. The predicted emissions agree reasonably well with the experimental data considering the range of operating points and given no adjustments of any rate constants have been employed. In an effort to identify CPU cost reduction potential, various dimensionality reduction strategies have been assessed. Furthermore, the sensitivity of the predictions with respect to resolution in particular relating to the CMC grid has been investigated. Overall, the results suggest that the presented modelling strategy has considerable predictive capability concerning Diesel engine combustion without requiring model constant calibration based on experimental data. This is true particularly for the heat release rates predictions and, to a lesser extent, for NOx emissions where further progress is still necessary. © 2009 SAE International.

Gaussian mixture modeling with Gaussian process latent variable models

Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low dimensional latent space, and a stochastic map to the observed space. We show how it can be interpreted as a density model in the observed space. However, the GPLVM is not trained as a density model and therefore yields bad density estimates. We propose a new training strategy and obtain improved generalisation performance and better density estimates in comparative evaluations on several benchmark data sets. © 2010 Springer-Verlag.

Variable repetition frequency femtosecond-pulse surface emitting semiconductor laser

We report a femtosecond-pulse vertical-external-cavity surface-emitting laser with a continuous repetition frequency tuning range of 8 near 1 GHz. A constant average output power of 56 ± 1 mW and near-transform-limited pulse duration of 450 ± 20 fs were observed across the entire tuning range. © 2011 American Institute of Physics.