13416968
Automatic capitalisation generation for speech input
Two different systems are proposed for the task of capitalisation generation. The first system is a slightly modified speech recogniser. In this system, every word in the vocabulary is duplicated: once in a de-capitalised form and again in capitalised forms. In addition, the language model is re-trained on mixed case texts. The other system is based on Named Entity (NE) recognition and punctuation generation, since most capitalised words are the first words in sentences or NE words. Both systems are compared when every procedure is fully automated. The system based on NE recognition and punctuation generation shows better results by word error rate, by F-measure and by slot error rate than the system modified from the speech recogniser. This is because the latter system has a distorted language model and a sparser language model. The detailed performance of the system based on NE recognition and punctuation generation is investigated by including one or more of the following: the reference word sequences, the reference NE classes and the reference punctuation marks. The results show that this system is robust to NE recognition errors. Although most punctuation generation errors cause errors in this capitalisation generation system, the number of errors caused in capitalisation generation does not exceed the number of errors from punctuation generation. In addition, the results demonstrate that the effect of NE recognition errors is independent of the effect of punctuation generation errors for capitalisation generation.
3077616
A combined punctuation generation and speech recognition system and its performance enhancement using prosody
Abstract A punctuation generation system which combines prosodic information with acoustic and language model information is presented. Experiments have been conducted for both the reference text transcriptions and speech recogniser outputs. For the reference transcription, prosodic information of acoustic data is shown to be more useful than language model information. Several straightforward modifications of a conventional speech recogniser allow the system to produce punctuation and speech recognition hypotheses simultaneously. The multiple hypotheses produced by the automatic speech recogniser are then re-scored using prosodic information. When the prosodic information is incorporated, the F -measure (defined as harmonic mean of recall and precision) can be improved. This speech recognition system including punctuation gives a small reduction in word error rate on the 1-best speech recognition output including punctuation. An alternative approach for generating punctuation from the un-punctuated 1-best speech recognition output is also proposed. The results from these two alternative schemes are compared.
10913816
A Web Services-enabled marketplace architecture for negotiation process management
As the eBusiness environment becomes more pervasive and dynamic, negotiations between companies are required more frequently than ever. Despite its potential value and the progress in research, the adoption of negotiation systems has been slow in practice. We believe one reason for this is insufficient consideration of process management aspects such as process design, description, and deployment. Business negotiations must be approached from the process management perspective since they take place in the context of corporate processes such as procurement or sales.In this paper, we study system support and automation of business-to-business (B2B) negotiations from the process management perspective. We propose a Web Services-enabled marketplace architecture for negotiation process management and refine it by adding pattern-based process composition. We validate the concept by implementing the proposed architecture using BPEL4WS and evaluating it from various perspectives.
30033753
Investigating the audio-visual speech detection advantage
Abstract Seeing the moving face of the talker permits better detection of speech in noise compared to auditory only presentation, an Audio–Visual (AV) facilitation effect. Three experiments that used a masked speech detection task are reported. The experiments were designed to contrast two accounts of the AV facilitation effect (AV peak listening and AV grouping). In each experiment a different manipulation of the relationship between the auditory and visual signals was employed. The first experiment manipulated the sequencing of the visual and auditory information by presenting the displays time normal or time reversed. The results showed that AV facilitation only occurred for the time normal presentations where there was a high correlation between the AV signals. Experiment 2 examined the impact on AV facilitation of shifting the auditory signals earlier in time than its normal position (again with time normal and time reversed presentation). It was found that shifting the auditory component abolished the AV effect for both the time normal and reversed conditions. The final experiment examined the AV detection advantage using another situation in which the relationship between the AV signals differed. Two versions of AV speech produced by a virtual talker were investigated. In one version, based on text-to-speech synthesis, the video and auditory signals were more rapid than the human talker of Experiment 1. In the other version, the signals were lengthened to match the durations of the human talker. A small but reliable AV facilitation effect was only found for the second version. The results are consistent with a cross-modal peak listening account and are discussed in terms of constraints on the integration of auditory and visual speech.
2033362
Bounding the cost of learned rules
Abstract In this article we approach one key aspect of the utility problem in explanation-based learning (EBL)—the expensive-rule problem —as an avoidable defect in the learning procedure. In particular, we examine the relationship between the cost of solving a problem without learning versus the cost of using a learned rule to provide the same solution, and refer to a learned rule as expensive if its use is more costly than the original problem solving from which it was learned. The key idea we explore is that expensiveness is inadvertently and unnecessarily introduced into learned rules by the learning algorithms themselves . This becomes a particularly powerful idea when combined with an analysis tool which identifies these hidden sources of expensiveness, and modifications of the learning algorithms which eliminate them. The result is learning algorithms for which the cost of learned rules is bounded by the cost of the problem solving that they replace.
5951770
MetaCore: an application specific DSP development system
This paper describes the MetaCore system which is an ASIP (Application-Specific Instruction set Processor) development system targeted for DSP applications. The goal of MetaCore system is to offer an efficient design methodology meeting specifications given as a combination of performance, cost and design turnaround time.
MetaCore system consists of two major design stages: design exploration and design generation. In the design exploration stage, MetaCore system accepts a set of benchmark programs and a formal specification of ISA (Instruction Set Architecture), and estimates the hardware cost and performance for each hardware configuration being explored. Once a hardware configuration is chosen, the system helps generate a VLSI processor design in the form of HDL along with the application program development tools such as C compiler, assembler and instruction set simulator.
6065142
On effective data clustering in bitemporal databases
Temporal databases provide built-in supports for efficient recording and querying of time-evolving data. In this paper, data clustering issues in temporal database environment are addressed. Data clustering is one of the most effective techniques that can improve performance of a database system. However, data clustering methods for conventional databases do not perform well in temporal databases because there exist crucial differences between their query patterns. We propose a data clustering measure, called Temporal Affinity, that can be used for the clustering of temporal data. The temporal affinity, which is based on the analysis of query patterns in temporal databases, reflects the closeness of temporal data objects in viewpoints of temporal query processing. We perform experiments to evaluate the proposed measure. The experimental results show that a data clustering method with the temporal affinity works better than other methods.