AAM Based Features for Multiple Camera Visual Speech Recognition in Car Environment Article Swipe
Astik Biswas
,
Prasanna Kumar Sahu
,
Anirban Bhowmick
,
Mahesh Chandra
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2015.07.417
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2015.07.417
This paper presents an Active Appearance Model (AAM) based multiple camera visual speech recognition (VSR) method using the shape and appearance information extracted from jaw and lip region to enhance the performance in mobile car cabin environments. Consideration of visual features along with traditional acoustic features have been found to be promising in complex auditory environment. Furthermore recent researches on multiple camera fusion to take care of pose information have shown promising result over single camera only. In this work a series of visual speech recognition experiments are carried out to study the influence of side and central faced camera on multistream visual speech recognizer. To have better information on visual articulators(Lip, Jaw etc) shape and texture model is built to extract the visual feature. Four camera audio visual corpus AVICAR is used in this research work. Individual camera streams are fused to have four stream synchronous Hidden Markov Model (SHMM) visual speech recognizer. The performance of the visual speech recognizer is improved by analyzing the relative impact of central frontal cameras with respect to side frontal cameras. Significant improvement is found compared to conventional Discrete Cosine Based(DCT) based visual features.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.procs.2015.07.417
- OA Status
- diamond
- Cited By
- 6
- References
- 21
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- 10
- OpenAlex ID
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https://openalex.org/W2246503653Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.procs.2015.07.417Digital Object Identifier
- Title
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AAM Based Features for Multiple Camera Visual Speech Recognition in Car EnvironmentWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2015Year of publication
- Publication date
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2015-01-01Full publication date if available
- Authors
-
Astik Biswas, Prasanna Kumar Sahu, Anirban Bhowmick, Mahesh ChandraList of authors in order
- Landing page
-
https://doi.org/10.1016/j.procs.2015.07.417Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://doi.org/10.1016/j.procs.2015.07.417Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Hidden Markov model, Computer vision, Discrete cosine transform, Audio visual, Feature (linguistics), Speech recognition, Pattern recognition (psychology), Image (mathematics), Linguistics, Multimedia, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
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2021: 1, 2018: 1, 2017: 1, 2016: 2, 2015: 1Per-year citation counts (last 5 years)
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21Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.based | 8, 188 |
| abstract_inverted_index.built | 119 |
| abstract_inverted_index.cabin | 35 |
| abstract_inverted_index.faced | 98 |
| abstract_inverted_index.found | 48, 181 |
| abstract_inverted_index.fused | 141 |
| abstract_inverted_index.model | 117 |
| abstract_inverted_index.only. | 76 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.shape | 18, 114 |
| abstract_inverted_index.shown | 70 |
| abstract_inverted_index.study | 91 |
| abstract_inverted_index.using | 16 |
| abstract_inverted_index.work. | 136 |
| abstract_inverted_index.(SHMM) | 150 |
| abstract_inverted_index.AVICAR | 130 |
| abstract_inverted_index.Active | 4 |
| abstract_inverted_index.Cosine | 186 |
| abstract_inverted_index.Hidden | 147 |
| abstract_inverted_index.Markov | 148 |
| abstract_inverted_index.better | 107 |
| abstract_inverted_index.camera | 10, 61, 75, 99, 126, 138 |
| abstract_inverted_index.corpus | 129 |
| abstract_inverted_index.fusion | 62 |
| abstract_inverted_index.impact | 167 |
| abstract_inverted_index.method | 15 |
| abstract_inverted_index.mobile | 33 |
| abstract_inverted_index.recent | 57 |
| abstract_inverted_index.region | 27 |
| abstract_inverted_index.result | 72 |
| abstract_inverted_index.series | 81 |
| abstract_inverted_index.single | 74 |
| abstract_inverted_index.speech | 12, 84, 103, 152, 159 |
| abstract_inverted_index.stream | 145 |
| abstract_inverted_index.visual | 11, 39, 83, 102, 110, 123, 128, 151, 158, 189 |
| abstract_inverted_index.cameras | 171 |
| abstract_inverted_index.carried | 88 |
| abstract_inverted_index.central | 97, 169 |
| abstract_inverted_index.complex | 53 |
| abstract_inverted_index.enhance | 29 |
| abstract_inverted_index.extract | 121 |
| abstract_inverted_index.frontal | 170, 176 |
| abstract_inverted_index.respect | 173 |
| abstract_inverted_index.streams | 139 |
| abstract_inverted_index.texture | 116 |
| abstract_inverted_index.Discrete | 185 |
| abstract_inverted_index.acoustic | 44 |
| abstract_inverted_index.auditory | 54 |
| abstract_inverted_index.cameras. | 177 |
| abstract_inverted_index.compared | 182 |
| abstract_inverted_index.feature. | 124 |
| abstract_inverted_index.features | 40, 45 |
| abstract_inverted_index.improved | 162 |
| abstract_inverted_index.multiple | 9, 60 |
| abstract_inverted_index.presents | 2 |
| abstract_inverted_index.relative | 166 |
| abstract_inverted_index.research | 135 |
| abstract_inverted_index.analyzing | 164 |
| abstract_inverted_index.extracted | 22 |
| abstract_inverted_index.features. | 190 |
| abstract_inverted_index.influence | 93 |
| abstract_inverted_index.promising | 51, 71 |
| abstract_inverted_index.Appearance | 5 |
| abstract_inverted_index.Based(DCT) | 187 |
| abstract_inverted_index.Individual | 137 |
| abstract_inverted_index.appearance | 20 |
| abstract_inverted_index.recognizer | 160 |
| abstract_inverted_index.researches | 58 |
| abstract_inverted_index.Furthermore | 56 |
| abstract_inverted_index.Significant | 178 |
| abstract_inverted_index.experiments | 86 |
| abstract_inverted_index.improvement | 179 |
| abstract_inverted_index.information | 21, 68, 108 |
| abstract_inverted_index.multistream | 101 |
| abstract_inverted_index.performance | 31, 155 |
| abstract_inverted_index.recognition | 13, 85 |
| abstract_inverted_index.recognizer. | 104, 153 |
| abstract_inverted_index.synchronous | 146 |
| abstract_inverted_index.traditional | 43 |
| abstract_inverted_index.conventional | 184 |
| abstract_inverted_index.environment. | 55 |
| abstract_inverted_index.Consideration | 37 |
| abstract_inverted_index.environments. | 36 |
| abstract_inverted_index.articulators(Lip, | 111 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5101908831 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I16292982 |
| citation_normalized_percentile.value | 0.83679214 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |