Representation Based Meta-Learning for Few-Shot Spoken Intent Recognition Article Swipe
Ashish Mittal
,
Samarth Bharadwaj
,
Shreya Khare
,
Saneem Chemmengath
,
Karthik Sankaranarayanan
,
Brian Kingsbury
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.21437/interspeech.2020-3208
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.21437/interspeech.2020-3208
Spoken intent detection has become a popular approach to interface with\nvarious smart devices with ease. However, such systems are limited to the\npreset list of intents-terms or commands, which restricts the quick\ncustomization of personal devices to new intents. This paper presents a\nfew-shot spoken intent classification approach with task-agnostic\nrepresentations via meta-learning paradigm. Specifically, we leverage the\npopular representation-based meta-learning learning to build a task-agnostic\nrepresentation of utterances, that then use a linear classifier for prediction.\nWe evaluate three such approaches on our novel experimental protocol developed\non two popular spoken intent classification datasets: Google Commands and the\nFluent Speech Commands dataset. For a 5-shot (1-shot) classification of novel\nclasses, the proposed framework provides an average classification accuracy of\n88.6% (76.3%) on the Google Commands dataset, and 78.5% (64.2%) on the Fluent\nSpeech Commands dataset. The performance is comparable to traditionally\nsupervised classification models with abundant training samples.\n
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.21437/interspeech.2020-3208
- OA Status
- green
- Cited By
- 9
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3096277532
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3096277532Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21437/interspeech.2020-3208Digital Object Identifier
- Title
-
Representation Based Meta-Learning for Few-Shot Spoken Intent RecognitionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-10-25Full publication date if available
- Authors
-
Ashish Mittal, Samarth Bharadwaj, Shreya Khare, Saneem Chemmengath, Karthik Sankaranarayanan, Brian KingsburyList of authors in order
- Landing page
-
https://doi.org/10.21437/interspeech.2020-3208Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2106.15238Direct OA link when available
- Concepts
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Computer science, Leverage (statistics), Classifier (UML), Personalization, Artificial intelligence, Speech recognition, Natural language processing, Representation (politics), Task (project management), Machine learning, Spoken language, World Wide Web, Political science, Economics, Politics, Law, ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 3, 2022: 3, 2021: 3Per-year citation counts (last 5 years)
- References (count)
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33Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.dataset. | 93, 123 |
| abstract_inverted_index.evaluate | 71 |
| abstract_inverted_index.intents. | 36 |
| abstract_inverted_index.learning | 56 |
| abstract_inverted_index.leverage | 52 |
| abstract_inverted_index.personal | 32 |
| abstract_inverted_index.presents | 39 |
| abstract_inverted_index.proposed | 102 |
| abstract_inverted_index.protocol | 79 |
| abstract_inverted_index.provides | 104 |
| abstract_inverted_index.training | 134 |
| abstract_inverted_index.commands, | 26 |
| abstract_inverted_index.datasets: | 86 |
| abstract_inverted_index.detection | 2 |
| abstract_inverted_index.framework | 103 |
| abstract_inverted_index.interface | 9 |
| abstract_inverted_index.of\n88.6% | 109 |
| abstract_inverted_index.paradigm. | 49 |
| abstract_inverted_index.restricts | 28 |
| abstract_inverted_index.approaches | 74 |
| abstract_inverted_index.classifier | 68 |
| abstract_inverted_index.comparable | 127 |
| abstract_inverted_index.samples.\n | 135 |
| abstract_inverted_index.a\nfew-shot | 40 |
| abstract_inverted_index.performance | 125 |
| abstract_inverted_index.the\nFluent | 90 |
| abstract_inverted_index.the\npreset | 21 |
| abstract_inverted_index.utterances, | 62 |
| abstract_inverted_index.experimental | 78 |
| abstract_inverted_index.the\npopular | 53 |
| abstract_inverted_index.Specifically, | 50 |
| abstract_inverted_index.developed\non | 80 |
| abstract_inverted_index.intents-terms | 24 |
| abstract_inverted_index.meta-learning | 48, 55 |
| abstract_inverted_index.with\nvarious | 10 |
| abstract_inverted_index.Fluent\nSpeech | 121 |
| abstract_inverted_index.classification | 43, 85, 98, 107, 130 |
| abstract_inverted_index.novel\nclasses, | 100 |
| abstract_inverted_index.prediction.\nWe | 70 |
| abstract_inverted_index.quick\ncustomization | 30 |
| abstract_inverted_index.representation-based | 54 |
| abstract_inverted_index.traditionally\nsupervised | 129 |
| abstract_inverted_index.task-agnostic\nrepresentation | 60 |
| abstract_inverted_index.task-agnostic\nrepresentations | 46 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.84440732 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |