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Abstract—Mental health disorders are growing
exponentially, yet early identification is hindered by current
dependence on subjective evaluation. Responding to this, a realtime Speech Emotion Recognition (SER) system is conceived for
objective and constant monitoring of emotional states. The
system processes live input speech, extracting Mel-Frequency
Cepstral Coefficients (MFCCs) for extracting important
spectral and timbral features. The extracted features are used
for a hybrid Deep Belief Network–Long Short-Term Memory
(DBN-LSTM) model, wherein the DBN achieves deep feature
learning and the LSTM learns temporal relationships,
improving accuracy of emotion classification. The system
recognizes stress, anger, happiness, and neutral state emotions
and offers concrete information about a person's state of mind.
The overall aim is toward non-invasive, individualized
monitoring of mental health. The novelty of the framework
resides in synthesizing speech processing and a hybrid DBNLSTM structure for real-time emotion identification,
facilitating early intervention and virtual healthcare
applications.
Keywords:
Cite Article:
"Speech emotion recognition using matlab", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a109-a115, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604014.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator