Exploring the Effectiveness of Feature Reduction and Kernel-Based Matching for Query-by- Example Spoken Term Detection Using CNN Article Swipe
Manisha Naik Gaonkar
,
Veena Thenkanidiyoor
,
Dileep Aroor Dinesh
,
H Muralikrishna
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3520605
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3520605
Query-by-example spoken term detection (QbE-STD) refers to the search for an audio query in a repository of audio utterances. A common approach for QbE-STD involves computing a matching matrix between the feature representations of the query and the reference utterance and deciding the relevance of the reference utterance to the query based on the computed matching matrix. The time required to compute the matching matrix is crucial since a matching matrix must be computed between a query and every reference utterance. This time depends on the number of feature representations in the query and reference utterance. Feature reduction is a technique that reduces the number of feature representations to reduce the time required to compute a matching matrix. In this study, we propose to explore feature reduction in combination with kernel-based matching of reduced feature representation for query and reference utterances. We propose to decide the relevance of a reference utterance using a convolutional neural network (CNN) based classifier on the matching matrix. We demonstrate that the proposed approach not only results in a reduction in search time but also increases the accuracy of QbE-STD.
Related Topics
Concepts
Computer science
Kernel (algebra)
Term (time)
Reduction (mathematics)
Matching (statistics)
Artificial intelligence
Feature (linguistics)
Pattern recognition (psychology)
Query by Example
Web search query
Information retrieval
Search engine
Mathematics
Statistics
Philosophy
Geometry
Linguistics
Quantum mechanics
Combinatorics
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3520605
- OA Status
- gold
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405838422
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4405838422Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3520605Digital Object Identifier
- Title
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Exploring the Effectiveness of Feature Reduction and Kernel-Based Matching for Query-by- Example Spoken Term Detection Using CNNWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Manisha Naik Gaonkar, Veena Thenkanidiyoor, Dileep Aroor Dinesh, H MuralikrishnaList of authors in order
- Landing page
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https://doi.org/10.1109/access.2024.3520605Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2024.3520605Direct OA link when available
- Concepts
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Computer science, Kernel (algebra), Term (time), Reduction (mathematics), Matching (statistics), Artificial intelligence, Feature (linguistics), Pattern recognition (psychology), Query by Example, Web search query, Information retrieval, Search engine, Mathematics, Statistics, Philosophy, Geometry, Linguistics, Quantum mechanics, Combinatorics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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26Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W1998766338, https://openalex.org/W2126203737, https://openalex.org/W4238136833, https://openalex.org/W2041305643, https://openalex.org/W2888845873, https://openalex.org/W2614542633, https://openalex.org/W2944964836, https://openalex.org/W2766963455, https://openalex.org/W2043878967, https://openalex.org/W4388333514, https://openalex.org/W4362673652, https://openalex.org/W4388656293, https://openalex.org/W4240592325, https://openalex.org/W2598392802, https://openalex.org/W1974745151, https://openalex.org/W2296362153, https://openalex.org/W3007486152, https://openalex.org/W3082556527, https://openalex.org/W2888951652, https://openalex.org/W2791614272, https://openalex.org/W3127686677, https://openalex.org/W2109235804, https://openalex.org/W2933771351, https://openalex.org/W6608930086, https://openalex.org/W4399829605, https://openalex.org/W4401110437 |
| referenced_works_count | 26 |
| abstract_inverted_index.A | 19 |
| abstract_inverted_index.a | 14, 26, 68, 75, 99, 115, 148, 152, 173 |
| abstract_inverted_index.In | 118 |
| abstract_inverted_index.We | 141, 163 |
| abstract_inverted_index.an | 10 |
| abstract_inverted_index.be | 72 |
| abstract_inverted_index.in | 13, 90, 127, 172, 175 |
| abstract_inverted_index.is | 65, 98 |
| abstract_inverted_index.of | 16, 33, 44, 87, 105, 132, 147, 183 |
| abstract_inverted_index.on | 52, 84, 159 |
| abstract_inverted_index.to | 6, 48, 60, 108, 113, 123, 143 |
| abstract_inverted_index.we | 121 |
| abstract_inverted_index.The | 57 |
| abstract_inverted_index.and | 36, 40, 77, 93, 138 |
| abstract_inverted_index.but | 178 |
| abstract_inverted_index.for | 9, 22, 136 |
| abstract_inverted_index.not | 169 |
| abstract_inverted_index.the | 7, 30, 34, 37, 42, 45, 49, 53, 62, 85, 91, 103, 110, 145, 160, 166, 181 |
| abstract_inverted_index.This | 81 |
| abstract_inverted_index.also | 179 |
| abstract_inverted_index.must | 71 |
| abstract_inverted_index.only | 170 |
| abstract_inverted_index.term | 2 |
| abstract_inverted_index.that | 101, 165 |
| abstract_inverted_index.this | 119 |
| abstract_inverted_index.time | 58, 82, 111, 177 |
| abstract_inverted_index.with | 129 |
| abstract_inverted_index.(CNN) | 156 |
| abstract_inverted_index.audio | 11, 17 |
| abstract_inverted_index.based | 51, 157 |
| abstract_inverted_index.every | 78 |
| abstract_inverted_index.query | 12, 35, 50, 76, 92, 137 |
| abstract_inverted_index.since | 67 |
| abstract_inverted_index.using | 151 |
| abstract_inverted_index.common | 20 |
| abstract_inverted_index.decide | 144 |
| abstract_inverted_index.matrix | 28, 64, 70 |
| abstract_inverted_index.neural | 154 |
| abstract_inverted_index.number | 86, 104 |
| abstract_inverted_index.reduce | 109 |
| abstract_inverted_index.refers | 5 |
| abstract_inverted_index.search | 8, 176 |
| abstract_inverted_index.spoken | 1 |
| abstract_inverted_index.study, | 120 |
| abstract_inverted_index.Feature | 96 |
| abstract_inverted_index.QbE-STD | 23 |
| abstract_inverted_index.between | 29, 74 |
| abstract_inverted_index.compute | 61, 114 |
| abstract_inverted_index.crucial | 66 |
| abstract_inverted_index.depends | 83 |
| abstract_inverted_index.explore | 124 |
| abstract_inverted_index.feature | 31, 88, 106, 125, 134 |
| abstract_inverted_index.matrix. | 56, 117, 162 |
| abstract_inverted_index.network | 155 |
| abstract_inverted_index.propose | 122, 142 |
| abstract_inverted_index.reduced | 133 |
| abstract_inverted_index.reduces | 102 |
| abstract_inverted_index.results | 171 |
| abstract_inverted_index.QbE-STD. | 184 |
| abstract_inverted_index.accuracy | 182 |
| abstract_inverted_index.approach | 21, 168 |
| abstract_inverted_index.computed | 54, 73 |
| abstract_inverted_index.deciding | 41 |
| abstract_inverted_index.involves | 24 |
| abstract_inverted_index.matching | 27, 55, 63, 69, 116, 131, 161 |
| abstract_inverted_index.proposed | 167 |
| abstract_inverted_index.required | 59, 112 |
| abstract_inverted_index.(QbE-STD) | 4 |
| abstract_inverted_index.computing | 25 |
| abstract_inverted_index.detection | 3 |
| abstract_inverted_index.increases | 180 |
| abstract_inverted_index.reduction | 97, 126, 174 |
| abstract_inverted_index.reference | 38, 46, 79, 94, 139, 149 |
| abstract_inverted_index.relevance | 43, 146 |
| abstract_inverted_index.technique | 100 |
| abstract_inverted_index.utterance | 39, 47, 150 |
| abstract_inverted_index.classifier | 158 |
| abstract_inverted_index.repository | 15 |
| abstract_inverted_index.utterance. | 80, 95 |
| abstract_inverted_index.combination | 128 |
| abstract_inverted_index.demonstrate | 164 |
| abstract_inverted_index.utterances. | 18, 140 |
| abstract_inverted_index.kernel-based | 130 |
| abstract_inverted_index.convolutional | 153 |
| abstract_inverted_index.representation | 135 |
| abstract_inverted_index.representations | 32, 89, 107 |
| abstract_inverted_index.Query-by-example | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.27250758 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |