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In this study, a new keyword spotting system (KWS) that utilizes phone confusion networks (PCNs) is presented. The new system exploits the compactness and accuracy of phone confusion networks to deliver fast and accurate results. Special design considerations are provided within the new algorithm to account for phone
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 this paper, a segmentation-free keyword spotting method is proposed for Bangla handwritten documents. In order to tolerate large variations in handwritten scenarios, we extracted key points based on SIFT key point detector, and the end and intersection points found by morphological operations. Heat Kernel signature
We propose a new segmentation-free method for keyword spotting in handwritten documents based on Heat Kernel Signature (HKS). After key points are located by the key point detector for SIFT on the document pages and the query image, HKS descriptors are extracted from a local patch centered at each key point. In order
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
This paper presents a new technique for preparing word templates to improve the performance of dynamic time warping based keyword spotting. The proposed technique selects one reference template from a small set of examples and in contrast to existing model based approaches does not require extensive training
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
We propose a script independent bayesian framework for keyword spotting in multilingual handwritten documents. The approach relies on local character level score and global word level hypothesis scores and learns a bayesian logistic regression classifier to distinguish between keywords and non-keywords. In a bayesian
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
Although keyword spotting (KWS) technologies have been successfully applied to some applications, most KWS systems have a common problem of noise-robustness when applied to real-world environments. Audio-visual keyword spotting (AVKWS) using both acoustic and visual information is a solution to complementarily solve
In this paper we describe the 2016 BBN conversational telephone speech keyword spotting system; the culmination of four years of research and development under the IARPA Babel program. The system was constructed in response to the NIST Open Keyword Search (OpenKWS) evaluation of 2016. We present our technological
integrated feature set is obtained after normalization of both sets of features thus obtained. This integrated feature set is used in a Hidden Markov Modeling (HMM) framework along with a novel sliding syllable protocol for keyword spotting. Keyword spotting experiments are conducted on the Hindi language database developed for
Keyword spotting refers to detection of all occurrences of any given word in a speech utterance. In this paper, we define the keyword spotting problem as a binary classification problem and propose a discriminative approach for solving it. Our approach exploits evolutionary algorithm to determine the separating hyper
spontaneous speech with momentous word error rate, which is a negative aspect of standard retrieval system. To prevail over such a constraint, we propose a method for spoken document retrieval based on spoken keyword spotting using Auto Associative Neural Networks (AANN). The proposed work concerns the exploit of the
We propose the Bayesian Active Learning by Disagreement (BALD) model for keyword spotting in handwritten documents. In the context of keyword spotting in handwritten documents, the background text is all regions in the document that do not contain the keywords. The model tries to learn certain characteristics of the
Sports video highlight detection is a popular topic. A multi-layer sport event detection framework is described. In the mid-level of this framework, visual and audio keywords are created from low-level features and the original video is converted into a keyword sequence. In the high-level, the temporal pattern of
Keyword spotting is the task of identifying the occurrences of certain desired keywords in an arbitrary speech signal. Keyword spotting has many applications one of them is telephone routing. In particular, we consider a big company which receives thousands of telephone calls daily. We are interested with the
We present a novel approach to query-by-example keyword spotting (KWS) using a long short-term memory (LSTM) recurrent neural network-based feature extractor. In our approach, we represent each keyword using a fixed-length feature vector obtained by running the keyword audio through a word-based LSTM acoustic model
Keyword spotting in speech is a very well-researched problem, but there are almost no approaches for singing. Most speech-based approaches cannot be applied easily to singing because the phoneme durations in singing vary a lot more than in speech, especially the vowel durations. To represent expected phoneme durations
Keyword spotting (KWS) deals with the identification of keywords in unconstrained speech, which is a natural, straightforward and friendly way for human-robot interaction (HRI). Most keyword spotters have the common problem of noise-robustness when applied to real-world environment with dramatically changing noises
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