Supervised learning is a popular approach to text classification among the research community as well as within software development industry. It enables intelligent systems to solve various text analysis problems such as document organization, spam detection and report scoring. However, the extremely difficult and time intensive process of creating a training corpus makes it inapplicable to many...
Evaluating the accuracy of HMM-based and SVM-based spotters in detecting keywords and recognizing the true place of keyword occurrence shows that the HMM-based spotter detects the place of occurrence more precisely than the SVM-based spotter. On the other hand, the SVM-based spotter performs much better in detecting
related keywords as representative vectors for different sentiments, we use these vectors as the sentiment classifier for the testing set. We achieved results that are not only comparable to traditional methods like Naïve Bayes and SVM, but also outperform Latent Dirichlet Allocation, TF-IDF and its variant. It also
truth is, it still lacks significant research efforts in the area of Bengali Document Categorization. In the first phase of this paper a model has been designed that extracts keywords from a Bengali document. We crawled over 35000 news documents form popular Bengali newspapers and journals. Those documents have been
In this paper, we proposed a method to realize the recently developed keyword-aware grammar for LVCSR-based keyword search using weight finite-state automata (WFSA). The approach creates a compact and deterministic grammar WFSA by inserting keyword paths to an existing n-gram WFSA. Tested on the evalpart1 data of the
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
Visual words of Bag-of-Visual-Words (BoVW) framework are independent each other, which results in not only discarding spatial orders between visual words but also lacking semantic information. This study is inspired by word embeddings that a similar embedding procedure is applied to a large number of visual words. By this way, the corresponding embedding vectors of the visual words can be formulated...
In this work we aim to capitalize on the availability of Internet image search engines to automatically create image training sets from user provided queries. This problem is particularly difficult due to the low precision of image search results. Unlike many existing dataset gathering approaches, we do not assume a category model based on a small subset of the noisy data or an ad-hoc validation set...
Deep learning had a significant impact on diverse pattern recognition tasks in the recent past. In this paper, we investigate its potential for keyword spotting in handwritten documents by designing a novel feature extraction system based on Convolutional Deep Belief Networks. Sliding window features are learned from
In keyword spotting from handwritten documents, the word similarity is usually computed by combining character similarities. Converting similarity to probabilistic confidence is beneficial for context fusion and threshold selection. In this paper, we propose to directly estimate the posterior probability of candidate
Most traditional template matching based keyword recognition methods don't need training data, just rely on frame matching. However, the recognition speed is relatively slow and it can't be used in practice. The LVCSR-based method needs to convert the speech signal into text signal before recognition, which has an
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
sequence during training. This paper explores the design of an ASR-free end-to-end system for text query-based keyword search (KWS) from speech trained with minimal supervision. Our E2E KWS system consists of three sub-systems. The first sub-system is a recurrent neural network (RNN)-based acoustic auto-encoder trained to
In this paper, we propose a neural network based distance metric learning method for a better discrimination in the sequence-matching based keyword search (KWS). In this technique, we conduct a version of Dynamic Time Warping (DTW) based similarity search on the speaker independent posteriorgram space. With this, we
families, alphabets, phone sets and vocabulary sizes. In particular, it looks at ensembles of stimulated networks to ensure that improved generalisation will withstand system combination effects. In order to assess stimulated training beyond 1-best transcription accuracy, this paper looks at keyword search as a proxy for
The H-KWS 2016, organized in the context of the ICFHR 2016 conference aims at setting up an evaluation framework for benchmarking handwritten keyword spotting (KWS) examining both the Query by Example (QbE) and the Query by String (QbS) approaches. Both KWS approaches were hosted into two different tracks, which in
corpus. Using a bigram phoneme language model, phoneme recognition experiments are performed on a two hour independent test set using the Viterbi decoding which show a relative 33.3% improvement by our CD-DNN acoustic model. We then present a filler based Hybrid DNN-HMM Keyword Spotting KWS system which to our knowledge is
We explore techniques to improve the robustness of small-footprint keyword spotting models based on deep neural networks (DNNs) in the presence of background noise and in far-field conditions. We find that system performance can be improved significantly, with relative improvements up to 75% in far-field conditions
This paper proposes a method for keyword spotting in offline Chinese handwritten documents using a statistical model. On a text query word, the method measures the similarity between the query word and every candidate word in the document by combining a character classifier and four classifiers characterizing the
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