Modality Dropout for Multimodal Device Directed Speech Detection using Verbal and Non-Verbal Features Article Swipe
Gautam Krishna
,
Sameer Dharur
,
Oggi Rudovic
,
Pranay Dighe
,
Saurabh Adya
,
Ahmed Hussen Abdelaziz
,
Ahmed H. Tewfik
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.15261
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.15261
Device-directed speech detection (DDSD) is the binary classification task of distinguishing between queries directed at a voice assistant versus side conversation or background speech. State-of-the-art DDSD systems use verbal cues, e.g acoustic, text and/or automatic speech recognition system (ASR) features, to classify speech as device-directed or otherwise, and often have to contend with one or more of these modalities being unavailable when deployed in real-world settings. In this paper, we investigate fusion schemes for DDSD systems that can be made more robust to missing modalities. Concurrently, we study the use of non-verbal cues, specifically prosody features, in addition to verbal cues for DDSD. We present different approaches to combine scores and embeddings from prosody with the corresponding verbal cues, finding that prosody improves DDSD performance by upto 8.5% in terms of false acceptance rate (FA) at a given fixed operating point via non-linear intermediate fusion, while our use of modality dropout techniques improves the performance of these models by 7.4% in terms of FA when evaluated with missing modalities during inference time.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2310.15261
- https://arxiv.org/pdf/2310.15261
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387946985
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- OpenAlex ID
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https://openalex.org/W4387946985Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2310.15261Digital Object Identifier
- Title
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Modality Dropout for Multimodal Device Directed Speech Detection using Verbal and Non-Verbal FeaturesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-10-23Full publication date if available
- Authors
-
Gautam Krishna, Sameer Dharur, Oggi Rudovic, Pranay Dighe, Saurabh Adya, Ahmed Hussen Abdelaziz, Ahmed H. TewfikList of authors in order
- Landing page
-
https://arxiv.org/abs/2310.15261Publisher landing page
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https://arxiv.org/pdf/2310.15261Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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-
https://arxiv.org/pdf/2310.15261Direct OA link when available
- Concepts
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Prosody, Modalities, Modality (human–computer interaction), Computer science, Speech recognition, Conversation, Inference, Dropout (neural networks), Artificial intelligence, Natural language processing, Psychology, Machine learning, Communication, Social science, SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.deployed | 62 |
| abstract_inverted_index.directed | 13 |
| abstract_inverted_index.improves | 122, 152 |
| abstract_inverted_index.modality | 149 |
| abstract_inverted_index.acoustic, | 31 |
| abstract_inverted_index.assistant | 17 |
| abstract_inverted_index.automatic | 34 |
| abstract_inverted_index.detection | 2 |
| abstract_inverted_index.different | 105 |
| abstract_inverted_index.evaluated | 165 |
| abstract_inverted_index.features, | 39, 95 |
| abstract_inverted_index.inference | 170 |
| abstract_inverted_index.operating | 139 |
| abstract_inverted_index.settings. | 65 |
| abstract_inverted_index.acceptance | 132 |
| abstract_inverted_index.approaches | 106 |
| abstract_inverted_index.background | 22 |
| abstract_inverted_index.embeddings | 111 |
| abstract_inverted_index.modalities | 58, 168 |
| abstract_inverted_index.non-linear | 142 |
| abstract_inverted_index.non-verbal | 91 |
| abstract_inverted_index.otherwise, | 46 |
| abstract_inverted_index.real-world | 64 |
| abstract_inverted_index.techniques | 151 |
| abstract_inverted_index.investigate | 70 |
| abstract_inverted_index.modalities. | 84 |
| abstract_inverted_index.performance | 124, 154 |
| abstract_inverted_index.recognition | 36 |
| abstract_inverted_index.unavailable | 60 |
| abstract_inverted_index.conversation | 20 |
| abstract_inverted_index.intermediate | 143 |
| abstract_inverted_index.specifically | 93 |
| abstract_inverted_index.Concurrently, | 85 |
| abstract_inverted_index.corresponding | 116 |
| abstract_inverted_index.classification | 7 |
| abstract_inverted_index.distinguishing | 10 |
| abstract_inverted_index.Device-directed | 0 |
| abstract_inverted_index.device-directed | 44 |
| abstract_inverted_index.State-of-the-art | 24 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |