Complementary Language Model and Parallel Bi-LRNN for False Trigger Mitigation Article Swipe
Rishika Agarwal
,
Xiaochuan Niu
,
Pranay Dighe
,
Srikanth Vishnubhotla
,
Sameer Badaskar
,
Devang Naik
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.21437/interspeech.2020-3238
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.21437/interspeech.2020-3238
False triggers in voice assistants are unintended invocations of the assistant, which not only degrade the user experience but may also compromise privacy. False trigger mitigation (FTM) is a process to detect the false trigger events and respond appropriately to the user. In this paper, we propose a novel solution to the FTM problem by introducing a parallel ASR decoding process with a special language model trained from "out-of-domain" data sources. Such language model is complementary to the existing language model optimized for the assistant task. A bidirectional lattice RNN (Bi-LRNN) classifier trained from the lattices generated by the complementary language model shows a $38.34\%$ relative reduction of the false trigger (FT) rate at the fixed rate of $0.4\%$ false suppression (FS) of correct invocations, compared to the current Bi-LRNN model. In addition, we propose to train a parallel Bi-LRNN model based on the decoding lattices from both language models, and examine various ways of implementation. The resulting model leads to further reduction in the false trigger rate by $10.8\%$.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21437/interspeech.2020-3238
- OA Status
- green
- References
- 12
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3070633328
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3070633328Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21437/interspeech.2020-3238Digital Object Identifier
- Title
-
Complementary Language Model and Parallel Bi-LRNN for False Trigger MitigationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2020Year of publication
- Publication date
-
2020-10-25Full publication date if available
- Authors
-
Rishika Agarwal, Xiaochuan Niu, Pranay Dighe, Srikanth Vishnubhotla, Sameer Badaskar, Devang NaikList of authors in order
- Landing page
-
https://doi.org/10.21437/interspeech.2020-3238Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2008.08113Direct OA link when available
- Concepts
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Computer science, Decoding methods, Language model, Classifier (UML), Process (computing), False positive rate, Artificial intelligence, Natural language processing, Speech recognition, Algorithm, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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12Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.for | 82 |
| abstract_inverted_index.may | 19 |
| abstract_inverted_index.not | 12 |
| abstract_inverted_index.the | 9, 15, 32, 40, 51, 77, 83, 94, 98, 108, 114, 127, 143, 164 |
| abstract_inverted_index.(FS) | 121 |
| abstract_inverted_index.(FT) | 111 |
| abstract_inverted_index.Such | 71 |
| abstract_inverted_index.also | 20 |
| abstract_inverted_index.both | 147 |
| abstract_inverted_index.data | 69 |
| abstract_inverted_index.from | 67, 93, 146 |
| abstract_inverted_index.only | 13 |
| abstract_inverted_index.rate | 112, 116, 167 |
| abstract_inverted_index.this | 43 |
| abstract_inverted_index.user | 16 |
| abstract_inverted_index.ways | 153 |
| abstract_inverted_index.with | 61 |
| abstract_inverted_index.(FTM) | 26 |
| abstract_inverted_index.False | 0, 23 |
| abstract_inverted_index.based | 141 |
| abstract_inverted_index.false | 33, 109, 119, 165 |
| abstract_inverted_index.fixed | 115 |
| abstract_inverted_index.leads | 159 |
| abstract_inverted_index.model | 65, 73, 80, 101, 140, 158 |
| abstract_inverted_index.novel | 48 |
| abstract_inverted_index.shows | 102 |
| abstract_inverted_index.task. | 85 |
| abstract_inverted_index.train | 136 |
| abstract_inverted_index.user. | 41 |
| abstract_inverted_index.voice | 3 |
| abstract_inverted_index.which | 11 |
| abstract_inverted_index.detect | 31 |
| abstract_inverted_index.events | 35 |
| abstract_inverted_index.model. | 130 |
| abstract_inverted_index.paper, | 44 |
| abstract_inverted_index.$0.4\%$ | 118 |
| abstract_inverted_index.Bi-LRNN | 129, 139 |
| abstract_inverted_index.correct | 123 |
| abstract_inverted_index.current | 128 |
| abstract_inverted_index.degrade | 14 |
| abstract_inverted_index.examine | 151 |
| abstract_inverted_index.further | 161 |
| abstract_inverted_index.lattice | 88 |
| abstract_inverted_index.models, | 149 |
| abstract_inverted_index.problem | 53 |
| abstract_inverted_index.process | 29, 60 |
| abstract_inverted_index.propose | 46, 134 |
| abstract_inverted_index.respond | 37 |
| abstract_inverted_index.special | 63 |
| abstract_inverted_index.trained | 66, 92 |
| abstract_inverted_index.trigger | 24, 34, 110, 166 |
| abstract_inverted_index.various | 152 |
| abstract_inverted_index.compared | 125 |
| abstract_inverted_index.decoding | 59, 144 |
| abstract_inverted_index.existing | 78 |
| abstract_inverted_index.language | 64, 72, 79, 100, 148 |
| abstract_inverted_index.lattices | 95, 145 |
| abstract_inverted_index.parallel | 57, 138 |
| abstract_inverted_index.privacy. | 22 |
| abstract_inverted_index.relative | 105 |
| abstract_inverted_index.solution | 49 |
| abstract_inverted_index.sources. | 70 |
| abstract_inverted_index.triggers | 1 |
| abstract_inverted_index.$10.8\%$. | 169 |
| abstract_inverted_index.$38.34\%$ | 104 |
| abstract_inverted_index.(Bi-LRNN) | 90 |
| abstract_inverted_index.addition, | 132 |
| abstract_inverted_index.assistant | 84 |
| abstract_inverted_index.generated | 96 |
| abstract_inverted_index.optimized | 81 |
| abstract_inverted_index.reduction | 106, 162 |
| abstract_inverted_index.resulting | 157 |
| abstract_inverted_index.assistant, | 10 |
| abstract_inverted_index.assistants | 4 |
| abstract_inverted_index.classifier | 91 |
| abstract_inverted_index.compromise | 21 |
| abstract_inverted_index.experience | 17 |
| abstract_inverted_index.mitigation | 25 |
| abstract_inverted_index.unintended | 6 |
| abstract_inverted_index.introducing | 55 |
| abstract_inverted_index.invocations | 7 |
| abstract_inverted_index.suppression | 120 |
| abstract_inverted_index.invocations, | 124 |
| abstract_inverted_index.appropriately | 38 |
| abstract_inverted_index.bidirectional | 87 |
| abstract_inverted_index.complementary | 75, 99 |
| abstract_inverted_index."out-of-domain" | 68 |
| abstract_inverted_index.implementation. | 155 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7300000190734863 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.09068386 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |