7184753
Speech separation algorithms for multiple speaker environments
Conventional speaker identification and speech recognition algorithms do not perform well if there are multiple speakers in the background. For high performance speaker identification and speech recognition applications in multiple speaker environments, a speech separation stage is essential. Here we summarize the implementation of three speech separation techniques. Advantages and disadvantages of each method are highlighted, as no single method can work under all situations. Stand-alone software prototypes for these methods have been developed and evaluated.
123278
Enhanced speech in noisy multiple speaker environment
Noisy environments seriously degrade the performance of speech recognition systems. Here we implement a high performance speech enhancement algorithm. Data from speech separation challenge were used to evaluate the method. It was observed that the enhanced speech significantly improved the recognition performance. In 2 out of 4 SNR cases, over 100% relative percentage improvements were achieved. Standalone software prototype has been developed and evaluated.
16485753
An integrated approach to robust speaker identification and speech recognition
Conventional speaker identification and speech recognition algorithms cannot deal with noisy and multiple speaker environments. For example, IBM via Voice has low recognition rates if dictation is done in a noisy environment. In order to achieve high performance in speaker identification and speech recognition, we propose an integrated approach that takes every facet of the process into account. Here we summarize some preliminary results from the application of this integrated approach to robust speaker identification and speech recognition. A real-time stand-alone software prototype has been developed to evaluate the effectiveness of the approach.
14742588
Reducing Complexity of Software Deployment with Delta Configuration
Deploying a modern software service usually involves installing several software components, and configuring these components properly to realize the complex interdependencies between them. This process, which accounts for a significant portion of information technology (IT) cost, is complex and error-prone. In this paper, we propose delta configuration - an approach that reduces the cost of software deployment by eliminating a large number of choices on parameter values that administrators have to make during deployment. In delta configuration, the complex software stack of a distributed service is first installed and tested in a test environment. The resulting software images are then captured and used for deployment in production environments. To deploy a software service, we only need to copy these pre-configured software images into a production environment and modify them to account for the difference between the test environment and a production environment. We have implemented a prototype system that achieves software deployment using delta configuration of the configuration state captured inside virtual machines. We perform a case study to demonstrate that our scheme leads to substantial reduction in complexity for the customer, over the traditional software deployment method.
14835961
North-Holland On the existence of perfect Mendelsohn designs with k = 7 and A even
Let v, k and I be positive integers. A (v, k, A)-Mendelsohn design (briefly (v, k, A)-MD) is a pair (X, 3) where X is a v-set (of points) and !?8 is a collection of cyclically ordered k-subsets of X (called blocks) such that every ordered pair of points of X are consecutive in exactly il blocks of 3. A set of k distinct elements {a,, a-,, . . . , ak} is said to be cyclically ordered by a,Ca,C.‘<a&a, and the pair a,, a,,, are said to be t-apart in a cyclic k-tuple (a,, az, . . . , ak) where i + I is taken modulo k. If for all t = 1, 2, . . . , k 1, every ordered pair of points of X are t-apart in exactly )L blocks of 3, then the (v, k, A)-MD is called perfect and is denoted briefly by (v, k, A)-PMD. A necessary condition for the existence of a (v, k, A)-PMD is Av(v 1) = 0 (mod k). In this paper, we shall be concerned mostly with the case k = 7 and il even. It will be shown ttat the necessary condition for the existence of a (v, 7, A)-PMD, namely Av(v 1) = 0 (mod 7), is also sufficient for all even A 2 16, with at most 29 possible exceptions for the pair (v, A) where A is even and E. < 16. In the process, we shall also establish that the necessary condition v = 0 or 1 (mod 7) for the existence of a (v, 7, I)-PMD is also sufficient for all v 2 421 with at most 40 possible exceptions below this value, which improves the earlier results.
22049562
Existence of holey 3-GDDs of type (u, gtw1)
Abstract In this paper, the necessary and sufficient conditions for the existence of a group divisible design with block size three, u groups, t holes of size ug and one hole of size uw are given.
12880164
Packings and coverings of the complete directed multigraph with 3- and 4-circuits
Abstract Let λDK v denote the complete directed multigraph with v vertices, where any two distinct vertices x and y are joined by λ arcs ( x , y ) and λ arcs ( y , x ). By a k -circuit we mean a directed cycle of length k . In this paper, we consider the problem of finding maximal packings and minimal coverings of λDK v with k -circuits. In particular, we completely determine the packing number and covering number for the cases k = 3 and 4.
17275266
On Gauss quadrature and partial cross validation
New estimators of expected values Ew(X) of functions of a random variable X are introduced. The new estimators are based on Gauss quadrature, a numerical method frequently used to approximate integrals over finite intervals. The estimators need a small number of numerical evaluations and hence are useful in partial cross validation (PCV) a numerical method for finding optimal smoothing parameters in nonparametric curve estimation. The PCV can considerably reduce the computational cost of the generalized cross validation method typically used to determine the optimal smoothing parameter.