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Query-by-example spoken term detection using bottleneck feature and Hidden Markov model
2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2015Query by example spoken term detection (QbE-STD) is an effective search mechanism to find spoken queries in spoken audio, especially for the source limited language. The dynamic time warping (DTW) algorithm is the state-of-art algorithm in this area. This paper presents some methods to improve the QbE-STD performance.
Xue Liu, Wu Guo, Niansong Wang
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Novel methods for query selection and query combination in query-by-example spoken term detection
Proceedings of the 2010 international workshop on Searching spontaneous conversational speech, 2010Query-by-example (QbE) spoken term detection (STD) is necessary for low-resource scenarios where training material is hardly available and word-based speech recognition systems cannot be employed. We present two novel contributions to QbE STD: the first introduces several criteria to select the optimal example used as query throughout the search system.
Igor Szöke +2 more
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ACM Transactions on Information Systems, 2012
This article investigates query-by-example (QbE) spoken term detection (STD), in which the query is not entered as text, but selected in speech data or spoken. Two feature extractors based on neural networks (NN) are introduced: the first producing phone-state posteriors and the second making use of a compressive NN layer.
Frantisek Grezl +4 more
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This article investigates query-by-example (QbE) spoken term detection (STD), in which the query is not entered as text, but selected in speech data or spoken. Two feature extractors based on neural networks (NN) are introduced: the first producing phone-state posteriors and the second making use of a compressive NN layer.
Frantisek Grezl +4 more
openaire +2 more sources
Combined MFCC-FBCC Features for Unsupervised Query-by-Example Spoken Term Detection
2015A new set of features for addressing the problem of unsupervised spoken term detection is proposed in this paper. If we have a large audio database, the objective of this system is to find a spoken query in the databases. In unsupervised audio search, language specific resources are not required.
K. S. Riyas +3 more
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Alzheimer's and Dementia: Translational Research and Clinical Interventions, 2021
Sharon A Savage
exaly
Sharon A Savage
exaly
, 2021