Manjushree B. Aithal, Pooja R. Gaikwad and Shashikant L. Sahare
This paper deals with application of speech and emotion recognition using distorted speech signal. When speech signal is given as an input to any system some background noise always gets added to it which is undesirable. In order to beat this difficulty we change the signal using Principal Component Analysis and then the task of recognition is done using the Hidden Markov Models. So the developed system is capable of recognizing the speech and emotion from distorted speech signal by extracting the MFCCs. And then transformed using PCA to obtain eigen values. The eigenvalues with highest values contain important information which are retained and others are discarded as noise. Hidden Markov Models is most capable method used for speech and emotion recognition.