Garbage collection can be done in vector mode on supercomputers like the CRAY-2 and the Cyber 205. Both copying collection and mark-and-sweep can be expressed as breadth-first searches in which the "queue" can be processed in parallel. We have designed a copying garbage collector whose inner loop works entirely in vector mode. We give performance measurements of the algorithm as implemented for Lisp CONS cells on the Cyber 205. Vector-mode garbage collection performs up to nine times faster than scalar-mode collection--a worthwhile improvement.

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This paper describes the results of designing, training, and testing a neural network for the voiced/unvoiced speech classification problem. A feed-forward multilayer backpropagation network was used with 6 input, 10 intemal, and 2 output nodes -for a binary decision. The six feames are common and easily computed. Training was done with 72 frames from two speakers; testing was done with 479 frames from four speakers; a total of 2 errors (0.4%) occurred. Thus a small neural network performs well on the V / W problem. 1, Review of Previous ResearchVoiced/Unvoiced (VRnr) classifiers can been grouped into two general categories [SL77]: 1) classifiers which determine the VRnr content of a segment of speech as a byproduct of an attempt to determine the primary pitch of the segment, and 2) classifiers which determine the V / W content of a segment of speech by examining one or more speech signal features which are known to be correlated with the V/W distinction. This paper describes a neural network to do V/UV classification using the latter approach. Our motivation was to produce an accurate V / W classifier for high-quality analysis-synthesis using linear predictive coding.Multi-feature techniques have some drawbacks, and it is important to be aware of the practical limitations imposed by these methods. One problem with the techniques presented below is their lack of immunity to nonstationary noise effects [AR76, CBSO]. This problem is largely due to the fact that these methods do not incorporate some form of continuous adaptation to the environment being sampled-they involve training a V/UV classifier in a fixed noise environment, to operate in that same environment. For a discussion of V / W classification techniques designed to operate in the presence of varying noise conditions, see [CB80, KH87, BG87, KS791. In addition, the tradeoff between time and accuracy is important in many applications. In particular, it may not be practical to use a large number of features in real-time applications, especially if the features used are computationally complex. The ongoing development of digital signal processing hardware should cause a continuous reconsideration of such ?This work was supported in part by NSF Grant MIP-8705454. U. S. Army Research Office -Durham Contract DAAG29-85-K-0191.tradeoffs. In the analysis below, the techniques considered are applicable when high accuracy is desired, computation time is not a severe constraint, and noise effects can be determined in advance.Two issues are involved in building a V/W classifier of this general class. First, it is necessary to determine a set of speech features adequate to the V/UV decision task, and second, it is necessary to find an explicit rule for making the V / W decision based upon the values of the features. Although these two aspects cannot be completely isolated from one another, it is useful to view them as separate parts of the problem. (Sometimes, as in [SBSO], the set of features used is tailored to the classification method.) Separation allows a more sy...

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