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Keyword spotting becomes a very important branch of speech recognition. But the acoustic mismatch between training and testing environments often causes a severe degradation in the recognition performance. This paper presents an improved keyword spotting strategy. A fuzzy search algorithm is proposed to extract
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
We propose a new feature called Global Reoccurrence Measure for extracting keywords from a document. It expresses the characteristic of keywords that they reoccur frequently in local areas, and have wide coverage in a document. Experiments show that the global reoccurrence measure can improve the performance of
Keyword extraction is widely used for information indexing, compressing, summarizing, etc. Existing keyword extraction techniques apply various text-based algorithms and metrics to locate the keywords. At the same time, some types of audio and audiovisual content, e. g. lectures, talks, interviews and other speech
Keyword extraction aims to find representative phrases for a document. Graph-based keyword extraction represent the input document as a graph and rank its nodes according to their score using graph-based ranking method. In this paper, we propose a method to compute importance of co-occurrence word in document and
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
Internet is becoming an increasingly important platform for ordinary life and work. It is expected that keyword extraction can help people quickly find hot spots on the web, since keywords in a document provide important information about the content of the document. In this paper, we propose to use text clustering
We address the problem of keyword spotting in continuous speech streams when training and testing conditions can be different. We propose a keyword spotting algorithm based on sparse representation of speech signals in a time-frequency feature space. The training speech elements are jointly represented in a common
This paper presents a keyword extraction method of web pages based on domain thesaurus. The method extracts keywords from web pages based on traditional statistic features, such as frequency and location, and it also evaluates the weight of candidate keywords combining with their relation of domain thesaurus. This
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
In this paper we present our current work on automatic speaker recognition using keyword-conditioned phone N-gram modeling. We propose the use of contextual information around keywords in modeling a speaker's pronunciation characteristics at a phonetic level. Our approach is to add time margins around keywords when
In this paper, a method of automatic Chinese keyword extraction based on KNN is proposed. Firstly, it preprocesses the document by vector space model. Secondly, it constructs a set of candidate keywords based on KNN method and the labeled dataset. Finally, it post-processes on candidate keywords by the character of
This paper presents a keyword extraction technique that can be used for tracking topics over time. In our work, keywords are a set of significant words in an article that gives high-level description of its contents to readers. Identifying keywords from a large amount of on-line news data is very useful in that it can
Keyword extraction is an automated process that collects a set of terms, illustrating an overview of the document. The term is defined how the keyword identifies the core information of a particular document. Analyzing huge number of documents to find out the relevant information, keyword extraction will be the key
system be interpretable, it is necessary to select a group of keywords, or termed a keyword combination, to describe each text category. In this paper, we propose a novel algorithm, keyword combination extraction based on ant colony optimization (KCEACO), to search the optimal keyword combination of a target category. By
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