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In this work, a template-based search approach is adopted for the Keyword Search (KWS) problem on two of the low-resource languages (Turkish and Swahili). In low-resource languages, the use of Large Vocabulary Continuous Speech Recognition (LVCSR) systems in KWS tasks may perform poorly especially on out-of-vocabulary words. In the proposed method, the keywords are modeled to be in the same form of...
In this work, keyword search (KWS) is based on a symbolic index that uses posteriorgram representation of the speech data. For each query, sum-to-one normalization or keyword specific thresholding is applied to the search results. The effect of these methods on the proposed KWS system is investigated. Results are combined with a KWS system that is based on an index generated from automatic speech...
In this work, two different keyword search (KWS) methods are proposed in order to improve the existing KWS system which is based on large vocabulary continuous speech recognition (LVCSR) and weighted finite state transducers (WFST). In the first method, a symbolic index is generated by applying vector quantization to the posteriorgram representation of the audio and then WFST based search is performed...
Keyword search (KWS) systems based on automatic speech recognition lattices require sufficient amount of transcribed data. However, out of vocabulary queries are frequently encountered in low resource languages and they lower the KWS performance. One method to overcome this problem is to use confusion model (CM) that allows searching for expanded queries along with the original query. In this study,...
In this paper, we address the problem of defect detection in textile images, and present a novel hybrid method where independent vector analysis, a statistical method, is combined with wavelet transformation, a spectral method. Independent vector analysis, a generalization of independent component analysis, uses vectorized signals, thus, enables exploiting multiple datasets and offers a fully multivariate...
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