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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Vocal Vibes Speech Emotion Recognition using Machine Learning |
| Authors Name: | Sanskruti Gaikwad , Prerana Patil , Pritesh Gadiya , Vyankatesh Khetri |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_217234
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| Published Paper Id: | IJNRD2404046 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | http://doi.one/10.1729/Journal.38730 |
| Abstract: | One of the quickest and most natural ways for humans to communicate is through speech. Speech emotion recognition is the process of accurately anticipating a human's emotion from their speech. It improves the way people and computers communicate. Although it is tricky to annotate audio and difficult to forecast a person's sentiment because emotions are subjective, "Speech Emotion Recognition (SER)" makes this possible. Various researchers have created a variety of systems to extract the emotions from the speech stream. Speech qualities in particular are more helpful in identifying between various emotions, and if they are unclear, this is the cause of how challenging it is to identify an emotion from a speaker's speech. A variety of the datasets for speech emotions, its modelling, and types are accessible, and they aid in determining the style of speech. After feature extraction, the classification of speech emotions is a crucial component, so in this system proposal, we introduced Artificial Neural Networks (ANN model) that are utilised to distinguish emotions such as angry, disgust, Fear, happy, neutral, Sad and surprise. The proposed system model Artificial Neural Networks (ANN model) achieved training accuracy of 100% and Validation accuracy of 99%. |
| Keywords: | Emotion , Machine Learning , Speech |
| Cite Article: | "Vocal Vibes Speech Emotion Recognition using Machine Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.a354-a357, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404046.pdf |
| Downloads: | 00058 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2404046 Registration ID: 217234 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.38730 Page No: a354-a357 Country: Baner , Pune, Maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404046 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404046 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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