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parallelize loops. However, a main restriction of available tools for automatic loop parallelization is that the loops often have to be 'polyhedral' and that it is, e.g., not allowed to call functions from within the loops.In this paper, we present a seemingly simple extension to the C programming language which marks functions
backgrounds, geographical locations and situations. The working hypothesis of the project was that fuzzy mathematics combined with domain-specific data models, in other words, fuzzy ontologies, would help manage the uncertainty in finding information that matches the users needs. In this way, KNOWMOBILE places itself in the
In this paper we report our approaches to accomplishing the very limited resource keyword search (KWS) task in the NIST Open Keyword Search 2015 (OpenKWS15) Evaluation. We devised the methods, first, to attain better acoustic modeling, multilingual and semi-supervised acoustic model training as well as the examplar
One commonly used approach for language recognition is to convert the input speech into a sequence of tokens such as words or phones and then to use these token sequences to determine the target language. The language classification is typically performed by extracting N-gram statistics from the token sequences and
In the area of national language processing, performing machine learning technique on customer or movie review for sentiment analysis has been? frequently tried. While methods such as? support vector machine (SVM) were much favored in the 2000s, recently there is a steadily rising percentage of implementation with
In keyword spotting applications, language modeling directly affects the system performance, as well as the acoustical modeling. This study focuses on the effects of different language models on the keyword spotting performance on Turkish voice recordings. Three different systems, one of which is proposed by us, that
This paper compares the performance of keyword and machine learning-based chest x-ray report classification for Acute Lung Injury (ALI). ALI mortality is approximately 30 percent. High mortality is, in part, a consequence of delayed manual chest x-ray classification. An automated system could reduce the time to
Language Model (LM) constitutes one of the key components in Keyword Spotting (KWS). The rapid development of the World Wide Web (WWW) makes it an extremely large and valuable data source for LM training, but it is not optimal to use the raw transcripts from WWW due to the mismatch of content between the web corpus
The ability to compute top-k matches to eXtensible Markup Language (XML) queries is gaining importance owing to the increasing of large XML repositories. Current work on top-k match to XML queries mainly focuses on employing XPath, XQuery or NEXI as the query language, whereas little work has concerned on top-k match
A user who wants to get information from a relational database needs to know database schema and structured query languages like SQL. The ordinary users are not familiar to those things, so searching information from relational databases is hard to them. Keyword search is a solution of the problem, where a keyword
This paper presents a novel architecture for keyword spotting in spontaneous speech, in which keyword model is trained from a small number of acoustic examples provided by a user. The word-spotting architecture relies on scoring patch feature vector sequences extracted by using sliding windows, and performing keyword
augmentation, the multilingual bottleneck feature extractor trained from 6 languages, text selection from web data for language model training, semi-supervised training for acoustic models and language models, out-of-vocabulary keyword detection using morphemes and a rich diversity of the systems for combination. A wide variety
language resources (LR) for training acoustic models, there is a need for reducing model training costs to support new target languages. Particular cases of under-resourced languages pose even a greater challenge for PS as the available LR are not sufficient for acoustic model training. This study examines methods for keyword
single feature stream which is more beneficial to all languages than the unilingual features. In the case of balanced corpus sizes, the multilingual BN features improve the automatic speech recognition (ASR) performance by 3–5% and the keyword search (KWS) by 3–10% relative for both limited (LLP) and full
Facing high error rates and slow recognition speed for full text transcription of unconstrained handwriting images, keyword spotting is a promising alternative to locate specific search terms within scanned document images. We have previously proposed a learning-based method for keyword spotting using character hidden
Markov Model/ Artificial Neural Network (HMM/ANN) keyword spotting framework. The feature extraction method used was Mel-Frequency Cepstral Coefficients (MFCC). The ANN is a 3-layer feedforward neural network using Multi-Layer Perceptron (MLP). In recognizing the words, an HMM decoder was used which implemented the Viterbi
order to query, which is also user friendly to the end users. Here E-learning materials may be schema-free or poorly-schematized. In this proposed work, a graphical query formulation language, called MashQL, is used in order to easily query structured data in E-learning application. Even, when the end users have limited
perceptron learning to improve system performance. The entire suite of semi-supervised methods presented in this paper was evaluated under the IARPA Babel program for the keyword spotting tasks. Our semi-supervised system had the best performance in the OpenKWS13 surprise language evaluation for the limited condition. In this
++ algorithm is presented to extract keywords. The algorithm generates and extracts keywords from a poetry book. The C++ programming language is used to implement our algorithm in order to obtain experimental results. The results indicate that “Love”, “Heart”, and “Eyes” are
. Various training criteria were then explored to improved RNNLMs' efficiency in both training and evaluation. Significant and consistent improvements on both keyword search and ASR tasks were obtained across all languages.
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