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Performance varies substantially along the critical dimensions of vocabulary size, quality of articulation (eg, isolated words versus continuous coarticulated speech), and user-speci c training (none versus speaker-dependent session) In addition, noise backgrounds and acoustic scene complexity (eg, newsroom versus cocktail party) also affect performance In challenging scenes word error rates on the order of 40% are common Commercial speech processing software may achieve a 50% word error rate for general untrained speech or 20 30% for fully trained speaker-dependent recognizers in low background noise [249] Statistical language models can correct word errors to a lower phrase error rate 1024 Perceiving Noise and Interference Rarely do speech-to-text tools characterize the ackground noise, so if the CR is going to extract QoI cues from background noise (eg.

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, the sound of a train or the chirp of a bird), it will need further far- eld acoustic signal processing beyond speech recognition The AACR may be able to tell that the user is in or near a vehicle that is running based on its ambient sound signature, such as from a commuter aircraft An acoustic processing subsystem with VOX set for voice may remove such background noise which is really acoustic interference in order to obtain a clearer voice command from the user Although that may be a valuable contribution, the iCR algorithm also would characterize the strong background for the AACR association of scene and acoustic cues To the degree that specialized machinery exhibits characteristic patterns, the acoustic signal may characterize common equipment like elevators for acoustic cues Code Maker In Java Using Barcode generator for Java Related: .

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The figures for this example show a simple Java framework for pipelines and an example application The framework consists of a base class for pipeline tages, PipelineStage, shown in Fig 427, and a base class for pipelines, LinearPipeline, shown in Fig 428 Applications provide a subclass of PipelineStage for each desired stage, implementing its three abstract methods to indicate what the stage should do on the initial step, the computation steps, and the final step, and a subclass of LinearPipeline that implements its abstract methods to create an array containing the desired pipeline stages and the desired queues connecting the stages For the queue connecting the stages, we use LinkedBlockingQueue, an implementation of the BlockingQueue interface These classes are found in the javautil concurrent package These classes use generics to specify the type of objects the queue can hold For example, new LinkedBlockingQueue<String> creates a BlockingQueue implemented by an underlying linked list that can hold Strings The operations of interest are put, to add an object to the queue, and take, to remove an object, take blocks if the queue is empty The class CountDownLatch, also found in the javautilconcurrent package, is a simple barrier that allows the program to print a message when it has terminated Barriers in general, and CountDownLatch in particular, are discussed in the Implementation Mechanisms design space The remaining figures show code for an example application, a pipeline to sort integers Fig 429 is the required subclass of LinearPipeline, and Fig 430 is the required subclass of PipelineStage Additional pipeline stages to generate or read the input and to handle the output are not shown.