9822374
Initial Study on Automatic Identification of Speaker Role in Broadcast News Speech
Identifying a speaker's role (anchor, reporter, or guest speaker) is important for finding the structural information in broadcast news speech. We present an HMM-based approach and a maximum entropy model for speaker role labeling using Mandarin broadcast news speech. The algorithms achieve classification accuracy of about 80% (compared to the baseline of around 50%) using the human transcriptions and manually labeled speaker turns. We found that the maximum entropy model performs slightly better than the HMM, and that the combination of them outperforms any model alone. The impact of the contextual role information is also examined in this study.
8135643
Joint Segmentation and Classification of Dialog Acts in Multiparty Meetings
This paper investigates a scheme for joint segmentation and classification of dialog acts (DAs) of the ICSI Meeting Corpus based on hidden-event language models and a maximum entropy classifier for the modeling of word boundary types. Specifically, the modeling of the boundary types takes into account dependencies between the duration of a pause and its surrounding words. Results for the proposed method compare favorably with our previous work on the same task
7031742
The effect of pruning and compression on graphical representations of the output of a speech recognizer
Large vocabulary continuous speech recognition can benefit from an efficient data structure for representing a large number of acoustic hypotheses compactly. Word graphs or lattices have been chosen as such an efficient interface between acoustic recognition engines and subsequent language processing modules. This paper first investigates the effect of pruning during acoustic decoding on the quality of word lattices and shows that by combining different pruning options (at the model level and word level), we can obtain word lattices with comparable accuracy to the original lattices and a manageable size. In order to use the word lattices as the input for a post-processing language module, they should preserve the target hypotheses and their scores while being as small as possible. In this paper, we introduce a word graph compression algorithm that significantly reduces the number of words in the graphical representation without eliminating utterance hypotheses or distorting their acoustic scores. We compare this word graph compression algorithm with several other lattice size-reducing approaches and demonstrate the relative strength of the new word graph compression algorithm for decreasing the number of words in the representation. Experiments are conducted across corpora and vocabulary sizes to determine the consistency of the pruning and compression results.
924129
Reducing overfitting in genetic programming models for software quality classification
A high-assurance system is largely dependent on the quality of its underlying software. Software quality models can provide timely estimations of software quality, allowing the detection and correction of faults prior to operations. A software metrics-based quality prediction model may depict overfitting, which occurs when a prediction model has good accuracy on the training data but relatively poor accuracy on the test data. We present an approach to address the overfitting problem in the context of software quality classification models based on genetic programming (GP). The problem has not been addressed in depth for GP-based models. The presence of overfitting in a software quality classification model affects its practical usefulness, because management is interested in good performance of the model when applied to unseen software modules, i.e., generalization performance. In the process of building GP-based software quality classification models for a high-assurance telecommunications system, we observed that the GP models were prone to overfitting. We utilize a random sampling technique to reduce overfitting in our GP models. The approach has been found by many researchers as an effective method for reducing the time of a GP run. However, in our study we utilize random to reduce overfitting with the aim of improving the generalization capability of our GP models.
9568443
A computational model for repeated pattern perception using frieze and wallpaper groups
Humans have an innate ability to perceive symmetry, but it is not obvious how to automate this powerful insight. In this paper the mathematical theory of frieze and wallpaper groups is used to extract visually meaningful building blocks (motifs) from a repeated pattern. A novel peak detection algorithm based on "regions of dominance" is used to automatically detect the underlying translational lattice of a repeated pattern. Following automatic classification of the pattern's symmetry group, knowledge of the interplay between rotation, reflection, glide-reflection and translation in that group leads to a small set of candidate motifs that exhibit local symmetry consistent with the global symmetry of the entire pattern. Although other work has addressed detection of the translational lattice of a repeated pattern, ours is the first to seek a principled method for determining a representative motif. Experiments show that the-resulting pattern motifs conform well with human perception.
2394019
A computational model for periodic pattern perception based on frieze and wallpaper groups
We present a computational model for periodic pattern perception based on the mathematical theory of crystallographic groups. In each N-dimensional Euclidean space, a finite number of symmetry groups can characterize the structures of an infinite variety of periodic patterns. In 2D space, there are seven frieze groups describing monochrome patterns that repeat along one direction and 17 wallpaper groups for patterns that repeat along two linearly independent directions to tile the plane. We develop a set of computer algorithms that "understand" a given periodic pattern by automatically finding its underlying lattice, identifying its symmetry group, and extracting its representative motifs. We also extend this computational model for near-periodic patterns using geometric AIC. Applications of such a computational model include pattern indexing, texture synthesis, image compression, and gait analysis.
526218
A distributed paging RAM grid system for wide-area memory sharing
Memory-intensive applications often suffer from the poor performance of disk swapping when memory is inadequate. Remote memory sharing schemes, which provide a remote memory that is faster than the local hard disk, are able to improve the performance of such applications. Due to the limitation of being applicable within single clusters only, however, most of the previous remote memory mechanisms, such as the network memory scheme, fail to be extendable into a large scale, distributed, heterogeneous, and dynamic environment. In this work, we propose a service-oriented grid memory sharing scheme, distributed paging RAM grid (DPRG). We study the properties and criteria of large scale memory sharing, and then design major operations and optimizations to fit the usage of grid systems. We collect trace from our grid environment, and evaluate DPRG through comprehensive trace-driven simulations. Results show that DPRG significantly outperforms existing remote memory sharing schemes and supports grid computing applications effectively
258700
Cervical Cancer Detection Using SVM Based Feature Screening
We present a novel feature screening algorithm by deriving relevance measures from the decision boundary of Support Vector Ma- chines. It alleviates the "independence" assumption of traditional screen- ing methods, e.g. those based on Information Gain and Augmented Vari- ance Ratio, without sacrificing computational efficiency. We applied the proposed method to a bottom-up approach for automatic cervical cancer detection in multispectral microscopic thin PAP smear images. An initial set of around 4,000 multispectral texture features is effectively reduced to a computationally manageable size. The experimental results show significant improvements in pixel-level classification accuracy compared to traditional screening methods.