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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 keywords and has higher detection rate. In this paper, we propose a rule based combination...
In this paper, we present the results of evaluating the robustness to language change of a previously proposed keyword spotting system. We assessed the robustness of this system when trained on clean English dataset and tested on telephony Persian speech. To have better recognition rate on telephony data, we used Cepstral mean and variance normalization (CMVN) and Cepstral gain normalization (CGN)...
Keyword spotting refers to detection of all occurrences of any given keyword in input speech utterances. In this paper, we define a keyword spotter as a binary classifier that separates a class of sentences containing a target keyword from a class of sentences which do not include the target keyword. In order to discriminate the mentioned classes, an efficient classification method and a suitable...
Keyword spotting (KWS) refers to finding of all occurrences of the chosen words in speech utterances. One of known methods for KWS problem is phone lattice search (PLS). In this method, accuracy and speed of lattice search are most important aspects. One method used in PLS, is Minimum Edit Distance (MED) measure. While this measure increases detection rate, it also raises the false alarm rate. In...
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 plane between two classes: class of sentences containing the target keywords and...
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