• Jeevitha N
• Akhila R
• Bhavana R
• M Mounika
• Usman Aijaz N,Divya Jyothi

Speech emotion recognition is a challenging task, an extensive reliance as been placed on models that use audio features in building well-performing classifiers. In this project, we propose a model that utilizes the text data and audio signals to obtain a better understanding of speech data. As emotional dialogue is composed of sound and spoken content, our model encodes the information from audio and text sequences using neural networks and then combines the information from these sources to predict the emotion class. This architecture analyses speech data from the signal level to the language level, and it does utilizes the information within the data more comprehensively then models that focus on audio features. Extensive experiments are conducted to investigate the efficiency and properties of the proposed model.Our proposed model outperforms previous state of the art methods in assigning data to one of the three emotion categories (i.e angry,happy,sad) when the model is applied to the data set.

speech emotion recognition, deep learning, natural language processing, CNN.

"Speech and Text Emotion Recognition Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.k137-k144, June-2022, Available :http://www.jetir.org/papers/JETIR2206A15.pdf

"Speech and Text Emotion Recognition Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppk137-k144, June-2022, Available at : http://www.jetir.org/papers/JETIR2206A15.pdf