Emotion Analysis from Speech of Different Age Groups Article Swipe
Hemanta Kumar Palo
,
Mihir Narayan Mohanty
,
Mahesh Chandra
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.15439/2017r21
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.15439/2017r21
This Recognition of speech emotion based on suitable features provides age information that helps the society in different ways.As the length and shape of human vocal tract and vocal folds vary with age of the speaker, the area remains a challenge.Emotion recognition system based on speaker's age will help criminal investigators, psychologists and law enforcement agencies in dealing with different segments of the society.Particularly child psychologists, counselors can take timely preventive measures based on such recognition system.The area remains further complex since the recognition system trained for adult users performs poorer when it involves children.This has motivated the authors to move in this direction.A novel effort is made in this work to determine the age of speaker based on emotional speech prosody and clustering them using fuzzy c-means algorithm.The results are promising and we are able to demarcate the emotional utterances based on age.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.15439/2017r21
- https://annals-csis.org/proceedings/rice2017/drp/pdf/21.pdf
- OA Status
- diamond
- Cited By
- 9
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2621798381
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2621798381Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.15439/2017r21Digital Object Identifier
- Title
-
Emotion Analysis from Speech of Different Age GroupsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2017Year of publication
- Publication date
-
2017-06-09Full publication date if available
- Authors
-
Hemanta Kumar Palo, Mihir Narayan Mohanty, Mahesh ChandraList of authors in order
- Landing page
-
https://doi.org/10.15439/2017r21Publisher landing page
- PDF URL
-
https://annals-csis.org/proceedings/rice2017/drp/pdf/21.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://annals-csis.org/proceedings/rice2017/drp/pdf/21.pdfDirect OA link when available
- Concepts
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Law enforcement, Computer science, Vocal tract, Prosody, Emotion recognition, Cluster analysis, Speech recognition, Speaker recognition, Psychology, Natural language processing, Artificial intelligence, Political science, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2022: 2, 2021: 2, 2020: 2Per-year citation counts (last 5 years)
- References (count)
-
25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.speaker, | 35 |
| abstract_inverted_index.suitable | 7 |
| abstract_inverted_index.demarcate | 137 |
| abstract_inverted_index.determine | 112 |
| abstract_inverted_index.different | 17, 59 |
| abstract_inverted_index.emotional | 119, 139 |
| abstract_inverted_index.motivated | 96 |
| abstract_inverted_index.promising | 131 |
| abstract_inverted_index.speaker's | 45 |
| abstract_inverted_index.clustering | 123 |
| abstract_inverted_index.counselors | 66 |
| abstract_inverted_index.preventive | 70 |
| abstract_inverted_index.system.The | 76 |
| abstract_inverted_index.utterances | 140 |
| abstract_inverted_index.Recognition | 1 |
| abstract_inverted_index.direction.A | 103 |
| abstract_inverted_index.enforcement | 54 |
| abstract_inverted_index.information | 11 |
| abstract_inverted_index.recognition | 41, 75, 83 |
| abstract_inverted_index.algorithm.The | 128 |
| abstract_inverted_index.children.This | 94 |
| abstract_inverted_index.psychologists | 51 |
| abstract_inverted_index.investigators, | 50 |
| abstract_inverted_index.psychologists, | 65 |
| abstract_inverted_index.challenge.Emotion | 40 |
| abstract_inverted_index.society.Particularly | 63 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5075057448 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I193073490 |
| citation_normalized_percentile.value | 0.75609756 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |