A UTOMATIC SPEECH RECOGNITION SYSTEMSrely almost exclusively on the acoustic speech signal and, consequently, these systems often perform poorly in noisy environments [ I ] . Attempts to clean up the acoustic input have had limited success [2]. Another approach is to use other sources of speech information, such as visual speech signals. performance of the system was degraded by this eqrly encoding.The need for early categorization of speech signals can be traced to the computational limitations of currently available hardware. On a digital computer, the inherently analog visual speech signals must first be converted to digital format. Next, a significant amount of preprocessing and encoding must be performed before these signals can be compared to a stored set of patterns. Finally. the symbolic descriptions of the segmented visual signal stream are combined with the auditory symbol stream, using rules that require a significant amount of programming. The von Neumann architecture requires that all of these steps be performed sequentially. We propose an alternative method for processing visual speech signals, based on analog computation in a distributed network architecture. By using many interconnected processors working in parallel, large amounts ofdata can be handled concurrently. In addition to speeding up the computation, this approach does not require segmentation in the early stages of processing; rather, analog signals from the visual and auditory pathways would flow through networks in real time and would be combined directly in the final analog Very Large-scale Integration (VLSI) implementation.Results are presented from a series of experiments that use neural networks to process the visual speech signals of a male talker. In these preliminary cxperiments, the results are limited to static images of vowels. We demonstrate that these networks are able to extract speech information from the visual images, and that this information can be used to improve automatic vowel recognition. The first section of this article reviews the structure of speech, and its corresponding acoustic and visual signals. The next section describes the specific data that was used in our experiments along with the network architectures and algorithms. In the final section, we present the results of integrating the visual and auditory signals for vowel recognition in the presence of acoustic noise.
Widespread human action and behavior change is needed to achieve many conservation goals. Doing so at the requisite scale and pace will require the efficient delivery of outreach campaigns. Conservation gains will be greatest when efforts are directed toward places of high conservation value (or need) and tailored to critical actors. Recent strategic conservation planning has relied primarily on spatial assessments of biophysical attributes, largely ignoring the human dimensions. Elsewhere, marketers, political campaigns, and others use microtargeting—predictive analytics of big data—to identify people most likely to respond positively to particular messages or interventions. Conservationists have not yet widely capitalized on these techniques. To investigate the effectiveness of microtargeting to improve conservation, we developed a propensity model to predict restoration behavior among 203,645 private landowners in a 5,200,000 ha study area in the Chesapeake Bay Watershed (U.S.A.). To isolate the additional value microtargeting may offer beyond geospatial prioritization, we analyzed a new high‐resolution land‐cover data set and cadastral data to identify private owners of riparian areas needing restoration. Subsequently, we developed and evaluated a restoration propensity model based on a database of landowners who had conducted restoration in the past and those who had not (n = 4978). Model validation in a parallel database (n = 4989) showed owners with the highest scorers for propensity to conduct restoration (i.e., top decile) were over twice as likely as average landowners to have conducted restoration (135%). These results demonstrate that microtargeting techniques can dramatically increase the efficiency and efficacy of conservation programs, above and beyond the advances offered by biophysical prioritizations alone, as well as facilitate more robust research of many social–ecological systems.
Nguyen and Widrow (1990) demonstrated that a feedforward neural network could be trained to steer a tractor-trailer truck to a dock while backing up. The feedforward network they used to control the truck contained 25 hidden units and required tens of thousands of training examples. The training strategy was to slowly expand the region in which the controller could operate, by starting with positions close to the dock and after a few thousand iterations moving the truck a little farther away.We found that a very simple solution exists requiring only two hidden units in the controller. The solution was found by decomposing the problem into subtasks. The original goal was to use the solution to these subtasks to reduce training time. What we found was a complete solution. Nevertheless, this example demonstrates how building prior knowledge into the network can dramatically simplify the problem.The problem is composed of three subtasks. First, the truck must be oriented so that the trailer is nearly normal to the dock. This is accomplished by continuously driving Ltrailer to zero by tilting the cab in the proper direction. Then, having gotten itrailer to zero or near zero, the cab must be straightened out to keep it there. Thus a restoring spring constant on Ltrailer is needed to drive Ltrailer to 0, and a restoring spring constant on icab is needed to straighten out the cab as Ltrailer approaches 0. This subnetwork depends upon the values of itrailer and Lcab and is independent of position.Once the truck is correctly oriented, the remaining objective is to dock at Y = 0. An acceptable solution is found to be independent of X, as long as the truck is not started too close to the left edge. An X dependence could be introduced to amplify the movement to Y = 0 when the truck is closer to the dock. This X dependence is equivalent to turning up the gain on the transfer function, and would best be captured by a multiplicative control term (X times Y) using 0-T units.The truck and the controller are shown in Figure 1. The specific weights used were adjusted based on observed performance, balancing between sensitivity and damping. This controller was able to successfully *Current address: Bellcore MRE 2E-330, Morristown, NJ 07962-1910 USA.
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