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@article{163260,
        author = {Sanskruti Gaikwad and Prerana Patil and Pritesh Gadiya and Vyankaesh Khetri  and PROF. DR. MRS. SARITA DESHPANDE},
        title = {Speech Emotion detection using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {826-830},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=163260},
        abstract = {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 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, their 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 (ANNs) that are used to distinguish emotions such as anger, disgust, fear, happiness, neutrality, sadness, and surprise.

},
        keywords = {Detection, Speech Input, Feature Extraction},
        month = {},
        }