A Perspective Study on Speech Recognition

Abstract

Emotions play an extremely important role in human mental life. It is a medium of expression of one’s perspective or one’s mental state to others. Speech Emotion Recognition (SER) can be defined as extraction of the emotional state of the speaker from his or her speech signal. There are few universal emotions including Neutral, Anger, Happiness, and Sadness in which any intelligent system with finite computational resources can be trained to identify or synthesize as required. In this work spectral and prosodic features are used for speech emotion recognition because both of these features contain the emotional information. Mel-Frequency Cepstral Coefficients (MFCC) is one of the spectral features. Fundamental frequency, loudness, pitch and speech intensity and glottal parameters are the prosodic features which are used to model different emotions. The potential features are extracted from each utterance for the computational mapping between emotions and speech patterns. Pitch can be detected from the selected features, using which gender can be classified. The audio signal is filtered using a method known as feature extraction technique. In this article, the feature extraction technique for speech recognition and voice classification is analyzed and also centered to comparative analysis of different types of Mel frequency cepstral coefficients (MFCC) feature extraction method. The MFCC technique is used for deduction of noise in voice signals and also used for voice classification and speaker identification. The statistical results of the different MFCC techniques are discussed and finally concluded that the delta-delta MFCC feature extraction technique is better than the other feature extraction techniques..