• Rishu Kumar
• S.A. Badarkhe
• Sandeep Singh
• Tejas goje
• V.P. Niwane ,S.R Patil

If we want to achieve emotional-related response from some algorithm or other intelligent machines, the initial step is fetch precise emotion recognition. This project deals with the implementation with the deep learning model of Convolutional Neural Networks (CNN). The architecture which is primarily based on an image processing CNN, developed in Python using Keras api which is based on TensorFlow platform. The basic methods that lays the foundation for the classification of emotions recognition based on certain voice parameters is briefly described. As per obtained results, the model tries to obtain the average precision of 79.33% for five emotions namely (happy, fear, sad, neutral, anger), which is comparable with performances reported in scientific literature.

Speech recognition and classification Using Deep Learning

"Speech recognition and classification Using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.333-335, August-2022, Available :http://www.jetir.org/papers/JETIRFP06058.pdf

"Speech recognition and classification Using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. pp333-335, August-2022, Available at : http://www.jetir.org/papers/JETIRFP06058.pdf