Andreas G. Andreou

Heteroscedastic discriminant analysis and reduced rank HMMs for improved speech recognition

We present the theory for heteroscedastic discriminant analysis (HDA), a model-based generalization of linear discriminant analysis (LDA) derived in the maximum-likelihood framework to handle heteroscedastic-unequal variance-classifier models. We show how to estimate the heteroscedastic Gaussian model parameters jointly with the dimensionality reducing transform, using the EM algorithm. In doing so, we alleviate the need for an a priori ad hoc class assignment. We apply the theoretical results to the problem of speech recognition and observe word-error reduction in systems that employed both diagonal and full covariance heteroscedastic Gaussian models tested on the TI-DIGITS database.

Current-mode subthreshold MOS circuits for analog VLSI neural systems

An overview of the current-mode approach for designing analog VLSI neural systems in subthreshold CMOS technology is presented. Emphasis is given to design techniques at the device level using the current-controlled current conveyor and the translinear principle. Circuits for associative memory and silicon retina systems are used as examples. The design methodology and how it relates to actual biological microcircuits are discussed.

Translinear circuits in subthreshold MOS

In this paper we provide an overview of translinear circuit design using MOS transistors operating in subthreshold region. We contrast the bipolar and MOS subthreshold characteristics and extend the translinear principle to the subthreshold MOS ohmic region through a drain/source current decomposition. A front/back-gate current decomposition is adopted; this facilitates the analysis of translinear loops, including multiple input floating gate MOS transistors. Circuit examples drawn from working systems designed and fabricated in standard digital CMOS oriented process are used as vehicles to illustrate key design considerations, systematic analysis procedures, and limitations imposed by the structure and physics of MOS transistors. Finally, we present the design of an analog VLSI “translinear system” with over 590,000 transistors in subthreshold CMOS. This performs phototransduction, amplification, edge enhancement and local gain control at the pixel level.

Polarization imaging: principles and integrated polarimeters

Polarization is a general descriptor of light and contains information about reflecting objects that traditional intensity-based sensors ignore. Difficult computer vision tasks such as image segmentation and object orientation are made tractable with polarization vision techniques. Specularities, occluding contours, and material properties can be readily extracted if the Stokes polarization parameters are available. Astrophysicists employ polarization information to measure the spatial distribution of magnetic fields on the surface of the Sun. In the medical field, analysis of the polarization allows the diagnose of disease in the eyes. The retinae of most insect and certain vertebrate species are sensitive to polarization in their environment, but humans are blind to this property of light. Biologists use polarimeters to investigate behaviors of animals-vis-a-vis polarization-in their natural habitats. In this paper, we first present the basics of polarization sensing and then discuss integrated polarization imaging sensors developed in our laboratory.

Probabilistic synaptic weighting in a reconfigurable network of VLSI integrate-and-fire neurons

We present a scheme for implementing highly-connected, reconfigurable networks of integrate-and-fire neurons in VLSI. Neural activity is encoded by spikes, where the address of an active neuron is communicated through an asynchronous request and acknowledgement cycle. We employ probabilistic transmission of spikes to implement continuous-valued synaptic weights, and memory-based look-up tables to implement arbitrary interconnection topologies. The scheme is modular and scalable, and lends itself to the implementation of multi-chip network architectures. Results from a prototype system with 1024 analog VLSI integrate-and-fire neurons, each with up to 128 probabilistic synapses, demonstrate these concepts in an image processing task.

AER image filtering architecture for vision-processing systems

A VLSI architecture is proposed for the realization of real-time two-dimensional (2-D) image filtering in an address-event-representation (AER) vision system. The architecture is capable of implementing any convolutional kernel F(x,y) as long as it is decomposable into x-axis and y-axis components, i.e., F(x,y)=H(x)V(y), for some rotated coordinate system {x,y} and if this product can be approximated safely by a signed minimum operation. The proposed architecture is intended to be used in a complete vision system, known as the boundary contour system and feature contour system (BCS-FCS) vision model, proposed by Grossberg and collaborators. The present paper proposes the architecture, provides a circuit implementation using MOS transistors operated in weak inversion, and shows behavioral simulation results at the system level operation and some electrical simulations.

Liquid crystal polarization camera

We present a fully automated system which unites CCD camera technology with liquid crystal technology to create a polarization camera capable of sensing the partial linear polarization of reflected light from objects at pixel resolution. As polarization sensing not only measures intensity but also additional physical parameters of light, it can therefore provide a richer set of descriptive physical constraints for the understanding of images. Previously it has been shown that polarization cues can be used to perform dielectric/metal material identification, specular and diffuse reflection component analysis, as well as complex image segmentations that would be significantly more complicated or even infeasible using intensity and color alone. Such analysis has so far been done with a linear polarizer mechanically rotated in front of a CCD camera. The full automation of resolving polarization components using liquid crystals not only affords an elegant application, but significantly speeds up the sensing of polarization components and reduces the amount of optical distortion present in the wobbling of a mechanically rotating polarizer. In our system two twisted nematic liquid crystals are placed in front of a fixed linear polarizer placed in front of a CCD camera. The application of a series of electrical pulses to the liquid crystals in synchronization with the CCD camera video frame rate produces a controlled sequence of polarization component images that are stored and processed on Datacube boards. We present a scheme for mapping a partial linear polarization state measured at a pixel into hue, saturation and intensity producing a representation for a partial linear polarization image. Our polarization camera currently senses partial linear polarization and outputs such a color representation image at 5 Hz. The unique vision understanding capabilities of our polarization camera system are demonstrated with experimental results showing polarization-based dielectric/metal material classification, specular reflection and occluding contour segmentations in a fairly complex scene, and surface orientation constraints.

Vocal tract normalization in speech recognition: Compensating for systematic speaker variability

The performance of speech recognition systems is often improved by accounting explicitly for sources of variability in the data. In the SWITCHBOARD corpus, studied during the 1994 CAIP workshop [Frontiers in Speech Processing Workshop II, CAIP (August 1994)], an attempt was made to compensate for the systematic variability due to different vocal tract lengths of various speakers. The method found a maximum probability parameter for each speaker which mapped an acoustic model to the mean of the models taken from a homogeneous speaker population. The underlying acoustic model was that of a straight tube, and the parameter estimation was accomplished by warping the spectrum of each speaker linearly over a 20% range (actually accomplished by digitally resampling the data), and finding the maximum a posteriori probability of the data given the warp. The technique produces statistically significant improvements in accuracy on a speech transcription task using each of four different speech recognition systems. T...

Voiced-speech representation by an analog silicon model of the auditory periphery

An analog CMOS integration of a model for the auditory periphery is presented. The model consists of middle ear, basilar membrane, and hair cell/synapse modules which are derived from neurophysiological studies. The circuit realization of each module is discussed, and experimental data of each modules response to sinusoidal excitation are given. The nonlinear speech processing capabilities of the system are demonstrated using the voiced syllable |ba|. The multichannel output of the silicon model corresponds to the time-varying instantaneous firing rates of auditory nerve fibers that have different characteristic frequencies. These outputs are similar to the physiologically obtained responses. The actual implementation uses subthreshold CMOS technology and analog continuous-time circuits, resulting in a real-time, micropower device with potential applications as a preprocessor of auditory stimuli.

Polarization camera sensors

Abstract Recently, polarization vision has been shown to simplify some important image understanding tasks that can be more difficult to perform with intensity vision alone. This, together with the more general capabilities of polarization vision for image understanding, motivates the building of camera sensors that automatically sense and process polarization information. Described in this paper are a variety of designs for polarization camera sensors that have been built to automatically sense partial linearly polarized light, and computationally process this sensed polarization information at pixel resolution to produce a visualization of reflected polarization from a scene, and/or a visualization of physical information in a scene directly related to sensed polarization. The three designs for polarization camera sensors presented utilize (i) serial acquisition of polarization components using liquid crystals, (ii) parallel acquisition of polarization components using a stereo pair of cameras and a polarizing beamsplitter, and (iii) a prototype photosensing chip with three scanlines, each scanline coated with a particular orientation of polarizing material. As the sensory input to polarization camera sensors subsumes that of standard intensity cameras, they can potentially significantly expand the application potential of computer vision. A number of images taken with polarization cameras are presented, showing potential applications to image understanding, object recognition, circuit board inspection and marine biology.