Feature Optimization of Speech Emotion Recognition Article Swipe
Chunxia Yu
,
Ling Xie
,
Weiping Hu
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.4236/jbise.2016.910b005
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.4236/jbise.2016.910b005
Speech emotion is divided into four categories, Fear, Happy, Neutral and Surprise in this paper. Traditional features and their statistics are generally applied to recognize speech emotion. In order to quantify each feature’s contribution to emotion recogni-tion, a method based on the Back Propagation (BP) neural network is adopted. Then we can obtain the optimal subset of the features. What’s more, two new characteristics of speech emotion, MFCC feature extracted from the fundamental frequency curve (MFCCF0) and amplitude perturbation parameters extracted from the short- time av-erage magnitude curve (APSAM), are added to the selected features. With the Gaus-sian Mixture Model (GMM), we get the highest average recognition rate of the four emotions 82.25%, and the recognition rate of Neutral 90%.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.4236/jbise.2016.910b005
- http://www.scirp.org/journal/PaperDownload.aspx?paperID=70752
- OA Status
- diamond
- Cited By
- 5
- References
- 11
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2523097911
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2523097911Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.4236/jbise.2016.910b005Digital Object Identifier
- Title
-
Feature Optimization of Speech Emotion RecognitionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2016Year of publication
- Publication date
-
2016-01-01Full publication date if available
- Authors
-
Chunxia Yu, Ling Xie, Weiping HuList of authors in order
- Landing page
-
https://doi.org/10.4236/jbise.2016.910b005Publisher landing page
- PDF URL
-
https://www.scirp.org/journal/PaperDownload.aspx?paperID=70752Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://www.scirp.org/journal/PaperDownload.aspx?paperID=70752Direct OA link when available
- Concepts
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Surprise, Emotion recognition, Mel-frequency cepstrum, Speech recognition, Feature (linguistics), Computer science, Artificial neural network, Pattern recognition (psychology), Artificial intelligence, Feature extraction, Psychology, Communication, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
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2021: 2, 2019: 1, 2018: 1, 2017: 1Per-year citation counts (last 5 years)
- References (count)
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11Number 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.av-erage | 85 |
| abstract_inverted_index.emotion, | 66 |
| abstract_inverted_index.emotion. | 26 |
| abstract_inverted_index.emotions | 111 |
| abstract_inverted_index.features | 16 |
| abstract_inverted_index.quantify | 30 |
| abstract_inverted_index.selected | 93 |
| abstract_inverted_index.Gaus-sian | 97 |
| abstract_inverted_index.amplitude | 77 |
| abstract_inverted_index.extracted | 69, 80 |
| abstract_inverted_index.features. | 58, 94 |
| abstract_inverted_index.frequency | 73 |
| abstract_inverted_index.generally | 21 |
| abstract_inverted_index.magnitude | 86 |
| abstract_inverted_index.recognize | 24 |
| abstract_inverted_index.parameters | 79 |
| abstract_inverted_index.statistics | 19 |
| abstract_inverted_index.Propagation | 43 |
| abstract_inverted_index.Traditional | 15 |
| abstract_inverted_index.categories, | 6 |
| abstract_inverted_index.feature’s | 32 |
| abstract_inverted_index.fundamental | 72 |
| abstract_inverted_index.recognition | 106, 115 |
| abstract_inverted_index.contribution | 33 |
| abstract_inverted_index.perturbation | 78 |
| abstract_inverted_index.recogni-tion, | 36 |
| abstract_inverted_index.characteristics | 63 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.80691597 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |