3736444
Domain-specific keyphrase extraction
Document keyphrases provide semantic metadata characterizing documents and producing an overview of the content of a document. They can be used in many text-mining and knowledge management related applications. This paper describes a Keyphrase Identification Program (KIP), which extracts document keyphrases by using prior positive samples of human identified domain keyphrases to assign weights to the candidate keyphrases. The logic of our algorithm is: the more keywords a candidate keyphrase contains and the more significant these keywords are, the more likely this candidate phrase is a keyphrase. To obtain prior positive inputs, KIP first populates its glossary database using manually identified keyphrases and keywords. It then checks the composition of all noun phrases of a document, looks up the database and calculates scores for all these noun phrases. The ones having higher scores will be extracted as keyphrases.
16588936
Identifying important concepts from medical documents
Automated medical concept recognition is important for medical informatics such as medical document retrieval and text mining research. In this paper, we present a software tool called keyphrase identification program (KIP) for identifying topical concepts from medical documents. KIP combines two functions: noun phrase extraction and keyphrase identification. The former automatically extracts noun phrases from medical literature as keyphrase candidates. The latter assigns weights to extracted noun phrases for a medical document based on how important they are to that document and how domain specific they are in the medical domain. The experimental results show that our noun phrase extractor is effective in identifying noun phrases from medical documents, so is the keyphrase extractor in identifying important medical conceptual terms. They both performed better than the systems they were compared to.
6210562
Discriminative training and explicit duration modeling for HMM-based automatic segmentation
HMM-based automatic segmentation has been popularly used for corpus construction for concatenative speech synthesis. Since the most important reasons for the inaccuracy of HMM-based automatic segmentation are the HMM training criterion and duration control, we will study these particular issues. For the HMM training, we apply the discriminative training method and introduce a new criterion, named Minimum SeGmentation Error (MSGE). In this method, a loss function directly related to the segmentation error is defined, and parameter optimization is performed by the Generalized Probabilistic Descent (GPD) algorithm. For the duration control problem, we apply explicit duration models and propose a two-step-based segmentation method to solve the problem of computational cost, where the duration model is incorporated in a postprocessor procedure. From the experimental results, these two techniques significantly improve segmentation accuracy with different focuses, where the MSGE-based discriminative training focuses on improving the accuracy of sensitive boundary, i.e., a boundary where an error in segmentation is likely to cause a noticeable degradation in speech synthesis quality, and the explicit duration modeling focuses on eliminating large errors. After combining these two techniques, the error average was reduced from 6.86 ms to 5.79 ms on Japanese data, and from 8.67 ms to 6.61 ms on Chinese data. Simultaneously, the number of errors larger than 30 ms were reduced 25% and 51% on Chinese and Japanese data, respectively.
15918446
Gesture Recognition Using Quadratic Curves
This paper presents a novel method for human gesture recognition based on quadratic curves. Firstly, face and hands in the images are extracted by skin color and their central points are kept tracked by a modified Greedy Exchange algorithm. Then in each trajectory, the central points are fitted into a quadratic curve and 6 invariants from this quadratic curve are computed. Following these computations, a gesture feature vector composed of 6n such invariants is constructed, where n is the number of the trajectories in this gesture. Lastly, the gesture models are learnt from the feature vectors of gesture samples and an input gesture is recognized by comparing its feature vector with those of gesture models. In this gesture recognition method, the computational cost is low because the gesture duration does not need to be considered and only simple curvilinear integral and matrix computation are involved. Experiments on hip-hop dance show that our method can achieve a recognition rate as high as 97.65% on a database of 16 different gestures, each performed by 8 different people for 8 different times.
5502752
Natural Textures for Weather Data Visualization
In this paper we present a novel method to visualize weather data with multi-layer controllable texture synthesis. Texture possesses multiple principal perceptual channels, which makes it good at encoding multiple data attributes contained in weather data. The natural textures existed in the real world especially provide plenty of choices to encode the data with visually pleasing images. A controllable texture synthesis method is developed to generate a large amount of textures which change the appearances of their individual perceptual dimensions according to the underlying distribution of data attributes. In order to encode more data attributes we further propose multi-layer texture synthesis. The background and foreground textures are separately synthesized and then combined together for display. In the end, we apply our method to some real-world weather data and demonstrate its effectiveness with a user study
7783237
HMM-Based Emotional Speech Synthesis Using Average Emotion Model
This paper presents a technique for synthesizing emotional speech based on an emotion-independent model which is called “average emotion” model. The average emotion model is trained using a multi-emotion speech database. Applying a MLLR-based model adaptation method, we can transform the average emotion model to present the target emotion which is not included in the training data. A multi-emotion speech database including four emotions, “neutral”, “happiness”, “sadness”, and “anger”, is used in our experiment. The results of subjective tests show that the average emotion model can effectively synthesize neutral speech and can be adapted to the target emotion model using very limited training data.
12694498
An Improvement to Unscented Transformation
This paper proposes a new sigma point selection strategy to better capture the information of a probability distribution By doing so, the non-local sampling problem inherent in the original unscented transformation (UT) is fundamentally eliminated It is argued that the improved UT (IUT) outperforms the original UT at the cost of increased but comparable computation burden and will be useful in constructing a nonlinear filter.